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Level 3 automated vehicles (L3 AVs) enable the driver to perform non-driving tasks, taking over in an emergency. In recent years, studies have extensively discussed the influencing factors of L3 AV takeovers. Extensive literature review shows that L3 AV takeovers are affected by human factors, traffic environment, and automatic driving systems. On this basis, this study proposes a conceptual framework of L3 AV takeovers. The main findings of this study include the following: (1) non-driving tasks, non-driving posture, individual characteristics, and trust have an impact on takeover behavior; (2) high traffic density, poor road geometry, and extreme weather have a negative impact on the takeover; (3) multimodal interaction design can improve collection performance. Although the existing research has made rich achievements, there are still many challenges. The influence of human factors on takeover performance is controversial, the quantification standard of takeover influencing factors is insufficient, and the prediction accuracy needs to be improved. It is suggested to refine the criteria of driver participation in NDRT, formulate an effective measurement standard of driver fatigue, and develop a takeover prediction model combining driver status and traffic environment conditions. It provides a research basis for the formulation of laws, infrastructure construction, and human–computer interaction design. [ABSTRACT FROM AUTHOR]

The integration of Industry 4.0 and automated vehicles into the smart cities concept is a topical issue in the urbanization of cities and technological innovation within cities. As it is a relatively modern issue, many aspects of this field have not yet been explored; as a consequence, this paper is concerned with the search for synergies between Industry 4.0 and automated vehicles in smart city infrastructures. There is a lack of contributions in this field that summarize these synergies in a single article and address a wide range of aspects, including transport, energy, communication, and citizen participation. As the field lacks a complete and clear summary of what is already known, which would help multiple stakeholders, the authors decided to conduct this review. The article elucidates the above-stated aspects through a clear and in-depth literature review, which is complemented by specific examples from practice. Of course, the article also includes a description of the synergy potential and the impact on the inhabitants, the environment, and, last but not least, on the overall city life. The main hypothesis of this article is that the integration of Industry 4.0 technologies and automated vehicles within smart city infrastructure will result in significant improvements in transportation efficiency, resource utilization, and overall urban sustainability. The article discusses the positives and negatives of such integration, highlighting, on the one hand, the benefits in terms of reducing environmental impact and improving citizens' quality of life, but on the other hand, also highlighting the various ethical, legal, and social issues that such integrations may bring. Several methods have been used within the article, namely analysis, synthesis, comparison, and historical interpretation. The final discussion highlights the benefits, as well as the challenges, that such integration faces and must deal with if it is to be successful. It can be concluded that the synergistic potential of automated vehicles and Industry 4.0 in smart city infrastructure is enormous and that such integration offers promising solutions for enhancing transportation efficiency, energy management, and overall urban sustainability. It is also highlighted in the article that, in order to reap the benefits of such synergies, a wide-ranging collaboration of policymakers, industry stakeholders, and urban planners is needed. [ABSTRACT FROM AUTHOR]

Complex road environments threaten the safe operation of automated vehicles. Among these, adverse weather conditions and road geometries have particularly significant impacts. This study investigates LiDAR-based automated vehicles (LAVs) driving safety on vertical curved roads in adverse weather. A key methodology involves constructing a failure function that incorporates both the available sight distance (ASD) and the required stopping sight distance (RSD). This function is analyzed using a combined approach of neural networks and Monte Carlo simulations to quantitatively evaluate and generalize the reliability of LAVs under various conditions. The results reveal that variations in weather conditions and vertical curve radii significantly impact the ASD of LAVs, while the influence of speed is relatively minor. Notably, dense fog and rainfall can substantially reduce LAVs' ASD on vertical curves. Furthermore, the vehicle automation level and speed have a significant impact on driving safety, emphasizing the need for road and operational domain design tailored to LAVs under adverse weather conditions and vertical curve radii. [ABSTRACT FROM AUTHOR]

Urban Air Mobility (UAM) has emerged in response to increasing traffic demands. As UAM involves commercial flights in complex urban areas, well-established automation technologies are critical to ensure a safe, accessible, and reliable flight. However, the current level of acceptance of automation is insufficient. Therefore, this study sought to objectively detect the degree of human trust toward UAM automation. Electroencephalography (EEG) signals, specifically Event-Related Potentials (ERP), were employed to analyze and detect operators' trust towards automated UAM, providing insights into cognitive processes related to trust. A two-dimensional convolutional neural network integrated with an attention mechanism (2D-ACNN) was also established to enable the end-to-end detection of trust through EEG signals. The results revealed that our proposed 2D-ACNN outperformed other state-of-the-art methods. This work contributes to enhancing the trustworthiness and popularity of UAM automation, which is essential for the widespread adoption and advances in the UAM domain. [ABSTRACT FROM AUTHOR]

This paper proposes a theoretical model to discuss the capacity of heterogeneous saturated flow. A crucial indicator, platooning intensity, which represents the willingness of connected and autonomous vehicles to form platoons, is taken into consideration. The relationship between platooning intensity and the penetration rate of connected and autonomous vehicles is also evaluated. Numerical analysis is conducted based on relevant parameters, which further improves the proposed theoretical model. Finally, a microscopic simulation is used to verify the accuracy of the proposed model. The results indicate that both the speed and the market penetration rate have a significant impact on capacity; however, the impact is not linear. The slope of the speed-affected curve gradually decreases, whereas the slope of the market penetration rate-affected curve gradually increases. The impact of market penetration rate on theoretical capacity intensifies with the increase in speed. As the number of vehicles within a fleet increase, the weighted average of platooning intensity gradually tends towards the market penetration rate. The formulation offers important insights into traffic performance with heterogeneous flow. [ABSTRACT FROM AUTHOR]

Integrating autonomous vehicles (AVs) into urban areas poses challenges for transportation, infrastructure, building, environment, society, and policy. This paper goes beyond the technical intricacies of AVs and takes a holistic, interdisciplinary approach by considering the implications for urban design and transportation infrastructure. Using a complex methodology encompassing various software types such as Simulation of Urban Mobility (SUMO 1.17.0) and STREETMIX, the article explores the results of a simulation that anticipates the implementation of AVs through different market penetration scenarios. We investigate how AVs could enhance the efficiency of transportation networks, reducing congestion and potentially increasing the throughput. However, we also acknowledge the dynamic nature of the scenarios, as new mobility patterns emerge in response to this technological shift. Furthermore, we propose innovative urban design approaches that could harness the full potential of AVs, fostering the development of sustainable and resilient cities. By exploring these design strategies, we hope to provide valuable guidance for urban planners and policymakers as they navigate the challenges and opportunities presented by the integration of these advanced technologies. [ABSTRACT FROM AUTHOR]

The main purpose of a detection system is to ascertain the state of an individual's eyes, whether they are open and alert or closed, and then alert them to their level of fatigue. As a result of this, they will refrain from approaching an accident site. In addition, it would be advantageous for people to be promptly alerted in real time before the occurrence of any calamitous events affecting multiple people. The implementation of Internet-of-Things (IoT) technology in driver action recognition has become imperative due to the ongoing advancements in Artificial Intelligence (AI) and deep learning (DL) within Advanced Driver Assistance Systems (ADAS), which are significantly transforming the driving encounter. This work presents a deep learning model that utilizes a CNN–Long Short-Term Memory network to detect driver sleepiness. We employ different algorithms on datasets such as EM-CNN, VGG-16, GoogLeNet, AlexNet, ResNet50, and CNN-LSTM. The aforementioned algorithms are used for classification, and it is evident that the CNN-LSTM algorithm exhibits superior accuracy compared to alternative deep learning algorithms. The model is provided with video clips of a certain period, and it distinguishes the clip by analyzing the sequence of motions exhibited by the driver in the video. The key objective of this work is to promote road safety by notifying drivers when they exhibit signs of drowsiness, minimizing the probability of accidents caused by fatigue-related disorders. It would help in developing an ADAS that is capable of detecting and addressing driver tiredness proactively. This work intends to limit the potential dangers associated with drowsy driving, hence promoting enhanced road safety and a decrease in accidents caused by fatigue-related variables. This work aims to achieve high efficacy while maintaining a non-intrusive nature. This work endeavors to offer a non-intrusive solution that may be seamlessly integrated into current automobiles, hence enhancing accessibility to a broader spectrum of drivers through the utilization of facial movement analysis employing CNN-LSTM and a U-Net-based architecture. [ABSTRACT FROM AUTHOR]

Once trusted, automated vehicles (AVs) will gradually appear in urban areas. Such a transition is an opportunity in transport planning to control undesired impacts and possibly mitigate congestion at a time when both conventional vehicles (CVs) and AVs coexist. This paper deals with the complex transport decision problem of designing part of the network that is exclusive for AVs through a nonlinear programming model. The objective function minimises the costs of travel times where vehicles circulate under user equilibrium. The model evaluates the benefits of having an AVs-dedicated infrastructure and the associated costs from the detouring of CVs. Three planning strategies are explored: incremental, long-term and hybrid planning. The first creates a subnetwork evolving incrementally over time. The second reversely designs a subnetwork from the optimal solution obtained at a ratio of 90% AVs. The third limits the incremental planning towards that optimal long-term solution. The model is applied to the city of Delft, in the Netherlands. Two scenarios are analysed, with and without AV-dedicated roads, at several AV penetration rates. We find that implementing dedicated roads for AVs reduces the overall costs and congestion up to 16%. However, CV detouring is inevitable at later network stages, increasing the total distance travelled (up to 8%) and congestion in the surroundings of AV subnetworks. Concerning the planning strategies, incremental planning is appropriate for starting in the initial stages and is the strategy that most tackles CV detouring. The hybrid or the long-term strategies are more suitable to be applied after a ratio of 50% AVs, and the hybrid planning is the strategy that most reduces delay. [ABSTRACT FROM AUTHOR]

Humanlike driving is significant in improving the safety and comfort of automated vehicles. This paper proposes a personalized motion planning method with driver characteristics in longitudinal and lateral directions for highway automated driving. The motion planning is decoupled into path optimization and speed optimization under the framework of the Baidu Apollo EM motion planner. For modeling driver behavior in the longitudinal direction, a car-following model is developed and integrated into the speed optimizer based on a weight ratio hypothesis model of the objective functional, whose parameters are obtained by Bayesian optimization and leave-one-out cross validation using the driving data. For modeling driver behavior in the lateral direction, a Bayesian network (BN), which maps the physical states of the ego vehicle and surrounding vehicles and the lateral intentions of the surrounding vehicles to the driver's lateral intentions, is built in an efficient and lightweight way using driving data. Further, a personalized reference trajectory decider is developed based on the BN, considering traffic regulations, the driver's preference, and the costs of the trajectories. According to the actual traffic scenarios in the driving data, a simulation is constructed, and the results validate the human likeness of the proposed motion planning method. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed coordination scheme for connected vehicles, including automated vehicles (AVs) and manual vehicles (MVs), at signal-free intersections. The cooperation issue of vehicles at an intersection is formulated into a multi-objective optimization problem that aims to eliminate conflicts and improve traffic mobility and fuel economy. For this purpose, the future trajectories of AVs and MVs are predicted by the respective car-following models, and are shared with neighboring vehicles in conflict relationships. The proposed scheme optimizes the sum of the performance of AVs within the cooperative zone in a prediction horizon. A distributed optimization algorithm in the receding horizon is presented to obtain the local optimal solutions, and is tested in simulations with different demand levels and penetration rates of AVs. The results show that the proposed scheme reduces travel time by 29.7–45.5% and 34.5–49.2%, and decreases fuel consumption by 27.6–35.3% and 21.6–29.9% under 70–100% penetration rates of AVs, compared to the no-control operation and fixed-time signal control strategy. In addition, a comparison simulation with the strategy of jointly optimizing the vehicle trajectory and signal timing is conducted to evaluate the relative merits of the proposed scheme. [ABSTRACT FROM AUTHOR]

Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users' learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users' learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD). [ABSTRACT FROM AUTHOR]

Connected and Automated Vehicles (CAVs) offer the potential to improve roadway capacity and safety. Thus, improving road infrastructure condition could be prioritized to eliminate further degradation of the transportation infrastructure. In order to foster the adoption of CAVs, incentives can be used; but there is a need to identify what type of incentive would be most effective. To identify effective incentive types, this study uses electric vehicles (EV) and hybrid vehicles as a surrogate to CAVs because of the similarities in obstacles faced for wider adoption. This study then provides some recommendations by examining incentives offered in 15 different countries and by reviewing the literature on the effectiveness of incentive types. [ABSTRACT FROM AUTHOR]

Autonomous vehicles and shared mobility represent two fields of strong innovation in transportation research, and Shared Autonomous Vehicle (SAV) services have been studied as a new promising mobility system. Such services offer a strong potential especially when integrated with the Public Transport (PT) system, increasing its attractiveness by providing first- and last-mile connections to low-demand areas. This paper performs a systematic review on a niche of SAV-related literature, selecting articles that address PT-SAV integrations, the issue of how SAVs should be implemented together with already existing transit systems to enhance collective mobility. Papers have been classified firstly according to the methodological and modelling approaches used by researchers, and then according to the different operational schemes with which SAV systems can be deployed. Common findings have been reported and commented on, identifying the lack of articles aimed at determining the most suitable SAV service typology for specific contexts and network configuration. Many authors indicate that if SAVs are operated as feeder modes to mass transit, they might improve public transport connectivity. However, further research is needed to explore the efficacy of SAV systems as an opportunity to address first/last-mile PT planning issues. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control (MPC) approach for controlling automated vehicle steering during path tracking is presented. A (linear parameter-varying) LPV vehicle plant model including steering dynamics is proposed to determine the system evolution matrices. The steering dynamics are modeled in two different ways by using first-order lag and a second-order lag; the application of the first-order system resulted in a slightly more accurate path-following. Additionally, a cascade MPC structure is applied in which two MPCs are used; the second-order steering dynamics are separated from the path-following controller in a second MPC. Both steering system models and the cascade MPC are evaluated in simulation and on a test vehicle. The reference trajectory is calculated based on a fixed predefined path by transforming the necessary path segment to the vehicle ego coordinate system, thereby describing the reference for the path-following task in a novel way. The MPC method computes the optimal steering angle vector at each time step for following the path. The longitudinal dynamics is controlled separately by a PI controller. After simulation evaluation, experimental tests were conducted on a test vehicle on an asphalt surface. Both simulation and experimental results prove the effectiveness of the proposed reference definition method. The effect of the applied steering system models is evaluated. The inclusion of the steering dynamics in the prediction model resulted in a significant increase in controller performance. Finally, the computational requirements of the proposed control and modeling methods are also discussed. [ABSTRACT FROM AUTHOR]

The deployment of automated vehicles (AVs) has the potential to disrupt and fundamentally transform urban transportation. As their implementation becomes imminent on cities' streets, it is of great concern that no comprehensive strategies have been formulated to effectively manage and mitigate their potential negative impacts, particularly with respect to the components of the do no significant harm (DNSH) framework recently introduced in the EU taxonomy. The methodology employed comprises three steps: (i) An extensive literature review on the impact of AVs on the DNSH components; (ii) exploration of designing a coherent pro-active vision by integrating measures identified in the literature as key elements to mitigate the harm; and (iii) an interdisciplinary focus group (FG) to verify whether the impacts of AVs and potential mitigation measures for Bucharest are similar to those identified by the literature and integrated into the pro-active vision. The results suggest that while there are commonalities, variations exist in focus and perspective, underscoring the necessity of examining the mitigation measures encompassed in the vision through additional focus groups conducted in different cities. [ABSTRACT FROM AUTHOR]

The motion planning and control method of automated vehicles, as the key technology of automated vehicles, directly affects the safety, comfort, and other technical indicators of vehicles. The planning module is responsible for generating a vehicle driving path. The control module is responsible for driving the vehicle. In this study, we review the main methods and achievements in motion planning and motion control for automated vehicles. The advantages and disadvantages of various planning and control methods are comparatively analyzed. Finally, some predictions and summaries based on the existing research results and trends are proposed. Through this analysis, it is believed that various types of algorithms will be further integrated in the future to complement each other's strengths and weaknesses. The next area of research will be to establish more accurate vehicle models to describe vehicle motion, improve the generalization-solving ability of algorithms, and enhance the planning and control of integrated 'human-vehicle-road' traffic systems. [ABSTRACT FROM AUTHOR]

Automotive millimeter-wave radars are widely used to measure kinematic parameters of surrounding objects in the environmental perception of automated vehicles and typically operate in frequency bands of around 24 GHz and 77 GHz. The 24 GHz and 77 GHz automotive millimeter-wave radars are similar in their speed and range measurement principles; however, they are distinct in their accuracies when measuring the above kinematic parameters because of the different carrier frequencies and bandwidths. In order to evaluate the kinematic parameter measurement performances of automotive millimeter-wave radars for environmental perception, this paper proposes a calibration method and establishes a bi-objective simulation facility for the speed and range calibration of 24 GHz and 77 GHz automotive millimeter-wave radars. The feasibility of the proposed calibration method and the accuracy of the established bi-objective simulation facility are verified by the results obtained via experiments conducted on two 24 GHz and 77 GHz millimeter-wave radar samples. According to the speed and range calibration results of the two millimeter-wave radar samples, the 77 GHz millimeter-wave radar sample possesses superior speed measurement accuracy compared with the 24 GHz sample. Additionally, the range measurement errors of the 77 GHz millimeter-wave radar sample are much smaller than those of the 24 GHz sample. This indicates that the 77 GHz millimeter-wave radar sample is more accurate and reliable than the 24 GHz sample. Finally, the 77 GHz millimeter-wave radar sample was tested for its speed and range resolving ability. The results confirm that the bi-objective simulation facility can satisfy the calibration requirements of speed resolution up to 0.01 m/s and range resolution up to 0.1 m. The proposed calibration method and the established simulation facility can easily satisfy the speed, range and resolution calibration requirements of 24 GHz and 77 GHz millimeter-wave radars with high precision and wide range, and can achieve more economical, comprehensive and traceable performance testing on both 24 GHz and 77 GHz automotive millimeter-wave radars for environmental perception. [ABSTRACT FROM AUTHOR]

This paper explores the possibility of applying simulation models formalized in the macrosimulation approach to predict the effects from the presence of automated vehicles in our cities. It is based on the use of a robust equilibrium assignment model allowing us to obtain multiclass traffic flows, including automated vehicles (AVs) and conventional ones (CVs) on large real-sized road networks. This modelling framework has been successfully applied to the road network of the metropolitan area of Rome, allowing us to assess the effects of AVs in future traffic at increasing penetration rates and the effects of possible transport policies involving AVs. [ABSTRACT FROM AUTHOR]

Featured Application: Using the key parameters identified in this paper, researchers can improve their designs of take-over request (TOR) user interfaces by increasing or decreasing the influence of each key factor, thus contributing to greater safety and comfort in conditionally automated vehicles. In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5', 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs. [ABSTRACT FROM AUTHOR]

This study assessed the limitations of the EasyMile EZ10 Gen 3 low-speed automated vehicle (LSAV) while operating on public roadways. The primary interest was to evaluate the infrastructure elements that posed the greatest challenges for the LSAV. A route was chosen that would satisfy a legitimate transit need. This route included more operational complexity and higher traffic volumes than a typical EasyMile LSAV deployment. The results indicate that the LSAV operated at a lower-than-expected speed (6 to 8 mph), with a high frequency of disengagements, and a regular need for safety operator intervention. Four-way stop-sign controlled intersections, three-lane roads with a shared turning lane in the middle, open areas, and areas without clear markings were the most challenging for the LSAV. Some important considerations include the need to have LSAVs operate on roadways where other vehicles may pass more safely, or on streets with slower posted speed limits. Additionally, the low passenger capacity and inability to understand where passengers are located onboard make it hard for the LSAV to replace bus transits. Currently, the LSAV is best suited to provide first/last-mile services, short routes within a controlled access area, and fill in gaps in conventional transits. [ABSTRACT FROM AUTHOR]

Background: UAVs (Unmanned Aerial Vehicles) and SUGVs (Sidewalk Unmanned Ground Vehicles) are two prominent options to revolutionize last mile home delivery. However, there is no literature yet addressing a comprehensive assessment of them. To bridge this research gap, this paper aimed to compare UAVs to SUGVs in the context of urban parcel delivery from a practical, conceptual, technological, commercial, and environmental perspective. Methodology: Based on structured literature and web research, this paper provided a comparative status quo review of these two delivery concepts. We introduced a parameter-based cost calculus model to estimate the costs per shipment for each technology. To detect the key cost drivers, we applied a one-way sensitivity analysis, as well as a "full factorial design of experiment" approach. Results: These key cost drivers for both operations are the "number of vehicles per operator" and the "average beeline service radius". From today's commercial point of view, our model indicated better profitability of SUGVs. However, technical and regulatory developments may render different results in the future. As SUGVs emit significantly less noise than UAVs, we assume that SUGVs have an additional advantage for usage in autonomous urban last mile delivery from a resident's perspective. Conclusions: Both key cost drivers will significantly influence the commercial viability of unmanned home delivery services. Safety and security aspects will determine regulatory rules on "number of vehicles per operator". To increase the "average beeline service radius", UAVs could profit from mothership delivery concepts while SUGV delivery may co-use existing public transport infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP–PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm's performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP–PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP–PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP–PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the success of two different Human Machine Interfaces (HMIs) in attracting drivers' attention when they were engaged in a Non-Driving-Related Task (NDRT) during SAE Level 3 driving. We also assessed the value of each on drivers' perceived safety and trust. A driving simulator experiment was used to investigate drivers' response to a non-safety-critical transition of control and five cut-in events (one hard; deceleration of 2.4 m/s2, and 4 subtle; deceleration of ~1.16 m/s2) over the course of the automated drive. The experiment used two types of HMI to trigger a takeover request (TOR): one Light-band display that flashed whenever the drivers needed to takeover control; and one auditory warning. Results showed that drivers' levels of trust in automation were similar for both HMI conditions, in all scenarios, except during a hard cut-in event. Regarding the HMI's capabilities to support a takeover process, the study found no differences in drivers' takeover performance or overall gaze distribution. However, with the Light-band HMI, drivers were more likely to focus their attention to the road centre first after a takeover request. Although a high proportion of glances towards the dashboard of the vehicle was seen for both HMIs during the takeover process, the value of these ambient lighting signals for conveying automation status and takeover messages may be useful to help drivers direct their visual attention to the most suitable area after a takeover, such as the forward roadway. [ABSTRACT FROM AUTHOR]

Automated vehicles are expected to greatly boost traffic efficiency. However, how to estimate traffic breakdown probability for the mixed flow of autonomous vehicles and human driven vehicles around ramping areas remains to be answered. In this paper, we propose a stochastic temporal queueing model to reliably depict the queue dynamics of mixed traffic flow at ramping bottlenecks. The new model is a specified Newell's car-following model that allows two kinds of vehicle velocities and first-in-first-out (FIFO) queueing behaviors. The jam queue join time is supposed to be a random variable for human driven vehicles but a constant for automated vehicles. Different from many known models, we check the occurrence of significant velocity drop along the road instead of examining the duration of the simulated jam queue so as to avoid drawing the wrong conclusions of traffic breakdown. Monte Carlo simulation results show that the generated breakdown probability curves for pure human driven vehicles agree well with empirical observations. Having noticed that various driving strategy of automated vehicles exist, we carry out further analysis to show that the chosen car-following strategy of automated vehicles characterizes the breakdown probabilities. Further tests indicate that when the penetration rate of automated vehicles is larger than 20%, the traffic breakdown probability curve of the mixed traffic will be noticeably shifted rightward, if an appropriate car-following strategy is applied. This indicates the potential benefit of automated vehicles in improving traffic efficiency. [ABSTRACT FROM AUTHOR]

High-level Automated Vehicles (HAVs) are expected to improve traffic safety significantly. However, verifying and evaluating HAVs remains an open problem. Scenario-based testing is a promising method for HAV testing. Boundary scenarios exist around the performance boundary between critical and non-critical scenarios. Testing HAVs in these boundary scenarios is crucial to investigate why collisions cannot be avoided due to small changes in scenario parameters. This study proposes a methodology to generate diverse boundary scenarios to test HAVs. First, an approach is proposed to obtain at least one High-Performance Classifier (HPC) based on two classification algorithms that iteratively guide each other to find uncertain scenarios to improve their performance. Then, the HPC is exploited to find candidate scenarios highly likely to be boundary scenarios. To increase the efficiency of candidate scenario generation, a strategy based on local sampling is presented to find more diverse candidate scenarios based on a small number of them. Numerical experiments show that the HPCs acquired by the method proposed in this study can achieve a classification accuracy of 98% and 99% for random car-following and cut-in scenarios, respectively. Moreover, more than 86% of 271,744 candidate cut-in scenarios derived by local sampling are near the performance boundary. [ABSTRACT FROM AUTHOR]

The forthcoming arrival of automated vehicles (AV) on the roads requires the re-evaluation or even adaptation of existing infrastructures as they are currently designed on the basis of human perception. Indeed, advanced driver-assistance systems (ADAS) do not necessarily have the same needs as drivers to detect road markings. One of the main challenges related to AV is the optimisation of the vehicle–infrastructure pair in order to guarantee the safety of all users. In this context, we compared the performance of a vehicle equipped with an ADAS machine-vision system with a dynamic retroreflectometer during the daytime on a road section. Our results questioned the reliability of the literature thresholds of the luminance contrast ratio on a dry road under sunny conditions. Despite the presence of old and worn road markings, the ADAS camera was able to detect the edge lines in more than 90% of the cases. The non-detections were not related to the poor condition of the markings but to the environmental conditions or the complexity of the infrastructure. [ABSTRACT FROM AUTHOR]

In this study, we propose a lane-changing conflict management model based on game theory for automated vehicles. When a vehicle plans to change to the adjacent lane, and if there is a closely following vehicle on that lane, the following vehicle must sacrifice its speed to make space for the lane-changing vehicle, which means there are conflicts of interest between two vehicles. So far, there is no clear answer if the following vehicle should make space for the lane-changing vehicle. These individualistic lane-changing models may lead to suboptimal traffic flow or even traffic safety issues. To solve this problem, this study designed a model based on game theory to solve lane-changing conflicts between the lane-changing vehicle and the following vehicle in the target lane. When the two vehicles enter a lane-changing conflict, the payoffs of the two vehicles under various combinations of strategies were evaluated, and the final strategy and the acceleration for each vehicle were obtained based on the principle of benefit equilibrium. The simulation is conducted to analyze the game strategy of the lane-changing vehicle (LV) and the close rear vehicle (RV) in the process of lane-changing from different initial positions. The results show that, under the hypothesis scenario in the simulation, the strategy {changing a lane, avoiding } will be chosen when the RV is initially located in the range of [0, 40 m], while {not changing a lane, not avoiding} is more appropriate when the initial position of the RV is in the range of [41 m, 90 m]. [ABSTRACT FROM AUTHOR]

Debates around the future of work have (re-)emerged strongly as concerns about a new era of workplace automation grow from a plethora of narratives estimating the potential of machine substitution. This research aims to analyse how these perspectives dictate current discussions based on taken-for-granted assumptions about work. The analysis takes the lens of work psychology and delves into the case of automated vehicles (AVs), an oft-debated form of automation by its implications for the transport workforce. We explore how the debate is subdued to a deterministic "metrological universe", overloading the present epoch with certainty about imaginaries of "driverless futures". However, when we move our attention beyond the quantitative speculation of future employment and connect automation with the concrete socio-professional realities of transport workers, such imaginaries are deconstructed while key issues regarding the quality of work with AVs emerge. By arguing that such issues remain "forgotten" in dominant technological discussions, bipartite and tripartite actions towards decent work are gathered, considering the institutional constellation in road transport. As future research, we propose to challenge those governance models predicated on the language of AV acceptance, which ultimately places the responsibility of managing the uncertainties that AVs hold on the shoulders of transport workers. [ABSTRACT FROM AUTHOR]

The automation of our vehicles is an all-present topic with great benefits for society, particularly in the area of public transport and pilot projects of automated shuttle buses are already underway. However, they do not show the full potential of using them as a supplement to public transport, since single-occupancy registration of the vehicles usually allows only slow speeds and also requires a substitute driver on board. In our study, we aim to (1) examine the status quo of its user acceptance and (2) identify the roles of the operators and their tasks in automated urban shuttle buses. We conducted a mixed-method study including in-depth interviews, questionnaires, and in-the-field observations visiting pilot projects of the two most widespread pilot projects on German streets. Our results uncover the multiple roles and tasks the human operators currently assume. Furthermore, we developed design approaches for a digital companion substituting the operator in a long run and evaluated these concepts. A remote operator or a hologram were preferred solutions and we propose further design requirements for such companions. This work helps to understand the individual roles that operators currently occupy and provides a good basis for concepts of technologies that will perform these tasks in the future. [ABSTRACT FROM AUTHOR]

Mobility needs, expectations, and concerns vary across age groups and are closely linked to users' views on the future of the road transport system. Automated vehicles are expected to have a significant impact on the future of the road transport system, and pilot deployments are increasingly being tested in Europe and beyond, which is also thanks to the evolving regulatory landscape. As a result, several studies have started to analyse citizens' attitudes towards this technology. However, very few studies have focused on teenagers' views on automated vehicles, although today's children and teenagers could be among the first users of such vehicles. Studying teenagers and the way they envisage automated vehicles in the future is of particular significance in defining transport planning strategies and supporting upcoming policy orientations. To cover this gap, the present study aims to explore teenagers' views about automated vehicles and whether and how they could fit into their future transport setting. A series of on-line and face-to-face focus groups, a demonstration of an automated vehicle prototype, supporting engagement activities, and a post-pre survey were used to collect their views on the topic. The results show that even though the teenagers acknowledged the potential advantages, they also expressed concerns in relation to the interactions with other road users, to automated driving systems' reliability, to safety, and to data privacy. In particular, these safety concerns revealed an unwillingness on the part of the teenagers to be among the first users of automated vehicles. [ABSTRACT FROM AUTHOR]

The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the physics–intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios. [ABSTRACT FROM AUTHOR]

Model-based verification uses a model to reason about the correctness of a real system. This requires the model and the system to be conformant, such that verification results on the model can be transfered to the real system. Especially for hybrid systems, which combine discrete and continuous behavior, defining and checking conformance is a difficult task. In this work, we present reachset conformance for hybrid systems that transfers safety properties from a model to the real system. We show how a model can be adapted to be conformant to measurements of a real system and demonstrate this for a real autonomous vehicle. The obtained reachset conformant model can be used for the verification of safety-critical properties, such as collision avoidance. [ABSTRACT FROM AUTHOR]

Featured Application: The authors predict that the results of their work will prove helpful in designing HMIs for use in automated vehicles. The results of the analysis show the characteristics of the pictograms with the greatest potential to induce correct driver reaction. Understanding the information conveyed by symbols is extremely important from the point of view of road safety. The symbols used on road signs and integrated into the vehicle must be clear and legible. This is especially important given the lack of regulations and standardization in the field of modern driving automation systems. Manufacturers are allowed to choose the way of informing the driver about the need to intervene in a situation when the vehicle is unable to continue driving in automated mode. The authors proposed to assess the comprehension of the information displayed by several different icons used to inform the driver about the need to take control. For this purpose, they used the adapted ISO 9186 Comprehension Test. The research was carried out on two different groups of drivers—passenger car drivers and professional bus drivers. A total of nine different pictograms were tested. The results indicate that drivers associate the red color with information about danger. The same icon in different colors may have a different effect on drivers, often leading to wrong responses. [ABSTRACT FROM AUTHOR]

Automated vehicle (AVs) technology is advancing at a rapid pace, offering new options for school travel. Parents play a decisive role in the choice of their child's school travel mode. To enable primary and secondary school students to take AVs to and from school, it is necessary to understand the factors that affect parents' intentions toward the new school travel mode. This study has three primary aims: (1) Discovering parents' intentions to escort children by AV and their potential determinants. (2) Constructing the Hybrid Choice Model (HCM) to examine the effects of parents' socioeconomic attributes, psychological factors, and travel attributes on using AVs to escort their children. (3) Raising practical implications to accelerate AV applications in school travel. The findings suggested that knowledge of AVs is the most important factor influencing parental intentions. Perceived usefulness, attitude, and perceived risk had significant effects on parental intentions. The direct effects of public engagement and perceived ease of use on parental intentions were not significant. Finally, this research can provide decision-making support for the government to formulate measures to promote AV application in school travel. [ABSTRACT FROM AUTHOR]

A human-like driving model can help to improve the acceptance and safety of automated driving systems (ADS). To improve the performance of human-like driving and interaction with conventional vehicles of ADS, the speed behavior of left-turn vehicles at the signalized intersection was studied using natural driving data. In this study, 374 valid data points of left-turn snippets at signalized intersections were extracted and three phases were introduced based on the reaction behavior of braking, stopping, and accelerating in the left-turn process. Firstly, a one-way ANOVA was used to study the influence of traffic density, traffic light state, intersection type, and left-turn waiting area on the reaction position of each phase and the spatial distribution of the speed. The traffic light state and traffic density were the main significant effects. Furthermore, to analyze the spatial distribution of acceleration, a method of frequency contour was conducted. The butterfly-shaped frequency contour suggested that "the closer to the stop line, the higher the variation of acceleration". Finally, the driving parameters at each phase were further analyzed. The main results indicate the following: (1) The red traffic light will lead to a larger variation of acceleration, a larger maximum deceleration, a larger starting acceleration, and a larger maximum acceleration. (2) On the condition of dense traffic density, more stops and the duration of the stop–go phase may cause the time pressure, and the driver tends to choose a greater maximum acceleration. (3) The red traffic light leads to a further reaction distance of all three phases, whilst increased traffic density only increases the reaction distance of the stop. (4) Both the dense traffic density and red traffic light lead to an earlier reaction time. The findings can provide a basis for the design of human-like driving of left-turn driving assistance systems and improve the interaction with left-turn conventional vehicles. [ABSTRACT FROM AUTHOR]

As new mobility called automated vehicles (AVs) appears on the road, positive effects are expected, but in fact, unexpected adverse effects may arise due to the mixed traffic situation with human-driven vehicles (HVs). Prior to the commercialization of AVs, a preliminary review and preventive measures are required, and among them, the interaction between the existing vehicle and the new mobility and the interaction with the infrastructure must be considered. Therefore, we propose (i) the positive–negative effect of introducing AVs in a mixed traffic situation and (ii) the optimal operation plan for the dedicated lane for AVs. First, the effect of introducing AVs considering the interaction between vehicles in the mixed traffic situation showed mostly positive such as speed increase, delay time reduction, and capacity increase. However, in a 75% Market Penetration Rate (MPR) environment of all levels of Service (LOS), the effect was diminished compared to the previous MPR. This is contemplated to be the result of a conflict caused by the operation of some HVs (including heavy vehicles) behavior as obstacles in the situation where most of the vehicles on the road are AVs. Based on the previous result, we deployed the dedicated lane to resolve the negative effect in the 75% MPR environment and proposed an optimal operation strategy for the AVs dedicated lane from the perspective of operational efficiency for a more feasible operation. Given the 75% MPR, the Mixed-Use operation strategy of High-Occupancy Vehicles (HOV) and AVs is ascertained as the most suitable operation strategy. This implies that even in the era of AVs, the influence of other vehicles (e.g., heavy vehicles, other mobility) must be considered. This study is significant by considering the negative effects of the introduction of AVs and presenting an optimal operation strategy for dedicated lanes, and it can expect to be used as a new strategy as part of the Free/Expressway Traffic Management System (FTMS) applicable in the era of autonomous driving. [ABSTRACT FROM AUTHOR]

Despite the technological advances in automated driving systems, traffic accidents involving automated vehicles (AVs) continue to occur, raising concerns over the safety and reliability of automated driving. For the smooth commercialization of AVs, it is necessary to systematically assess the driving safety of AVs under the various situations that they potentially face. In this context, these various situations are mostly implemented by using systematically developed scenarios. In accordance with this need, a scenario generation framework for the assessment of the driving safety of AVs is proposed by this study. The proposed framework provides a unified form of assessment with key components for each scenario stage to facilitate systematization and objectivity. The performance of the driving safety assessment scenarios generated within the proposed framework was verified. Traffic accident report data were used for verification, and the usefulness of the proposed framework was confirmed by generating a set of scenarios, ranging from functional scenarios to test cases. The scenario generation framework proposed in this study can be used to provide sustainable scenarios. In addition, from this, it is possible to create assessment scenarios for all road types and various assessment spaces, such as simulations, proving grounds, and real roads. [ABSTRACT FROM AUTHOR]

Automated Vehicles (AVs) are attracting attention as a safer mobility option thanks to the recent advancement of various sensing technologies that realize a much quicker Perception–Reaction Time than Human-Driven Vehicles (HVs). However, AVs are not entirely free from the risk of accidents, and we currently lack a systematic and reliable method to improve AV safety functions. The manual composition of accident scenarios does not scale. Simulation-based methods do not fully cover the peculiar AV accident patterns that can occur in the real world. Artificial Intelligence (AI) techniques are employed to identify the moments of accidents from ego-vehicle videos. However, most AI-based approaches fall short in accounting for the probable causes of the accidents. Neither of these AI-driven methods offer details for authoring accident scenarios used for AV safety testing. In this paper, we present a customized Vision Transformer (named ViT-TA) that accurately classifies the critical situations around traffic accidents and automatically points out the objects as probable causes based on an Attention map. Using 24,740 frames from Dashcam Accident Dataset (DAD) as training data, ViT-TA detected critical moments at Time-To-Collision (TTC) ≤ 1 s with 34.92 higher accuracy than the state-of-the-art approach. ViT-TA's Attention map highlighting the critical objects helped us understand how the situations unfold to put the hypothetical ego vehicles with AV functions at risk. Based on the ViT-TA-assisted interpretation, we systematized the composition of Functional scenarios conceptualized by the PEGASUS project for describing a high-level plan to improve AVs' capability of evading critical situations. We propose a novel framework for automatically deriving Logical and Concrete scenarios specified with 6-Layer situational variables defined by the PEGASUS project. We believe our work is vital towards systematically generating highly reliable and trustworthy safety improvement plans for AVs in a scalable manner. [ABSTRACT FROM AUTHOR]

The automated vehicle (AV) industry is a new growing industry of great interest. The environmental friendliness of AVs represents a core characteristic of their leap-forward development. The environmental effect of AVs, including its evaluation framework and algorithm, is a leading research area for continued technological innovation and market development in this field. This study focuses on three environmental aspects: the energy effect of AVs, the traffic effect of AVs on ground space, and the air effect of AVs on three-dimensional atmospheric space. First, an environmental effect evaluation indicator system that includes nine indices for AVs is constructed to be the basis for management decision making. Second, the interval-valued intuitionistic uncertain fuzzy (IVIUF) evaluation variable is used to solve the problem of data features that incorporate "crisp numbers" and "fuzzy numbers". Moreover, geometric weights are added based on the generalized correlation aggregation operator. Then, the IVIUL-weighted geometric (IVIULWG) operator is developed, which includes the objective and subjective information of "crisp numbers" and "fuzzy numbers" and makes the mathematical characteristics more scientific and accurate. Finally, an evaluation example is used to validate the effectiveness and practicability of the algorithm. [ABSTRACT FROM AUTHOR]

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence. [ABSTRACT FROM AUTHOR]

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges. [ABSTRACT FROM AUTHOR]

Transport automation is increasingly being studied from different perspectives; however, the perceptions of road haulage companies have received less attention. This study explores the views of representatives of small- and medium-sized road haulage companies on transport automation in Finland. We conducted an online survey to gather perceptions of automation, which received 254 responses from representatives of a range of different transport industries. The respondents' views towards automation were generally negative. The overall view was that automation may not be possible for heavy vehicles in Finland due to the adverse weather and driving conditions. The perception was that road haulage automation is unlikely to occur before 2050 in Finland. The results provide valuable insight for vehicle manufacturers, technology developers, policy makers, and haulage companies. As the road haulage industry is dominated by small- and medium-sized companies, hauliers should be supported in actively implementing new technologies. [ABSTRACT FROM AUTHOR]

How safe will Automated Vehicles (AVs) be? It depends on technologies, vehicles, other road users, and infrastructures. The only certainty is that automated vehicles will not solve all road safety issues. As the penetration of AVs might be much more sudden than road infrastructure modification, the infrastructure revision process should start immediately by then, and road authorities must be ready in turn. This paper presents an approach based on road safety inspections to quantify and compare the expected safety levels for a road section with different combinations of Automated and Conventional Vehicles (CVs). Developed in the framework of the EC project CoEXist, the proposed approach allows for identifying the possible safety issues that may arise when introducing AVs. All safety issues are judged in terms of risk, which is the product of the exposure, the likelihood of the crash, and the severity of the possible consequences. All these aspects are analyzed in different scenarios considering different AV penetration levels and driving logic. The results show a potential risk decrease according to the higher penetration of AVs. Nevertheless, the analysis highlights that safety issues may arise with the introduction of specific AVs' driving logics. [ABSTRACT FROM AUTHOR]

Around 90% of accidents stem from human error. Disruptive technology, especially automated vehicles (AVs), can respond to the problems by, for instance, eradicating human error when driving, thus increasing energy efficiency due to the platoon effect, and potentially giving more space to human activities by decreasing parking space; hence, with the introduction of the autonomous vehicle, the public attitude towards its adoption needs to be understood to develop appropriate strategies and policies to leverage the potential benefits. There is a lack of a systematic and comprehensive literature review on adoption attitudes toward AVs that considers various interlinked factors such as road traffic environment changes, AV transition, and policy impacts. This study aims to synthesize past research regarding public acceptance attitude toward AVs. More specifically, the study investigates driverless technology and uncertainty, road traffic environment changes, policy impact, and findings from AV adoption modelling approaches, to understand public attitudes towards AVs. The study points out critical problems and future directions for analysis of AV impacts, such as the uncertainty on AVs adoption experiment, policy implementation and action plans, the uncertainty of AV-related infrastructure, and demand modelling. [ABSTRACT FROM AUTHOR]

This study explores the long-term energy use implications of electrification, automation and sharing of road vehicles in British Columbia, Canada. Energy use is first analyzed for the years 1990–2016 for forward forecasting, and hypothetical scenarios ranging from conservative to disruptive, incorporating various effects of road vehicle electrification, sharing and automation, as well as influences of other technology disruptions, such as online shopping and e-learning are presented and used to project the road transportation energy use in B.C. to 2060. Transportation energy use projections are compared to those of the Canadian Energy Regulator (CER). When considering only the effect of vehicle electrification, the scenarios show higher energy savings compared to CER's scenarios. The combined impact of vehicle electrification and automation leads to decreased energy use to 2060 for all scenarios considered. The energy savings for all scenarios, except for the conservative one, are higher than CER's projections. When the effects of vehicle electrification, automation and sharing are merged, all scenarios yield energy savings beyond the CER projections. Inclusion of other technology disruptions and the effects of pandemics like COVID-19 reduce transportation demand and provide further energy savings. The BAU scenario given in this study shows energy use decreases compared to 2016 of 26.3%, 49%, 62.24%, 72.1% for the years 2030, 2040, 2050, and 2060 respectively. [ABSTRACT FROM AUTHOR]

As autonomous driving technology develops, the advantages and disadvantages of autonomous vehicles emerge. In order for automated vehicles to find a place in society, public opinion and acceptance are important, and a number of studies about public opinion and acceptance are underway. In this paper, we investigated the relation between cross cultural differences and public opinion on automated vehicles. Through a literature review, public opinion in various countries, including China, India, Japan, the U.S., the U.K., and Australia, was collected. Through these data, the influence of cross cultural differences in public opinion was identified, and statistical models for predicting public opinion about autonomous vehicles were developed. In addition, the prediction models were validated through the results of the survey conducted in this paper. As a result, the influence of cross cultural differences on public opinion about automated vehicles was confirmed. [ABSTRACT FROM AUTHOR]

Misconceptions of vehicle automation functionalities lead to either non-use or dangerous misuse of assistant systems, harming the users' experience by reducing potential comfort or compromise safety. Thus, users must understand how and when to use an assistant system. In a preliminary online survey, we examined the use, trust, and the perceived understanding of modern vehicle assistant systems. Despite remaining incomprehensibility (36-64%), experienced misunderstandings (up to 9%), and the need for training (around 30%), users reported high trust in the systems. In the following study with first-time users, we examine the effect of different User Onboarding approaches for an automated parking assistant system in a Tesla and compare the traditional text-based manual with a multimodal augmented reality (AR) smartphone application in means of user acceptance, UX, trust, understanding, and task performance. While the User Onboarding experience for both approaches shows high pragmatic quality, the hedonic quality was perceived significantly higher in AR. For the automated parking process, reported hedonic and pragmatic user experience, trust, automation understanding, and acceptance do not differ, yet the observed task performance was higher in the AR condition. Overall, AR might help motivate proper User Onboarding and better communicate how to operate the system for inexperienced users. [ABSTRACT FROM AUTHOR]

Future automated vehicles will have to meet the challenge of anticipating the intentions of other road users in order to plan their own behavior without compromising safety and efficiency of the surrounding road traffic. Therefore, the research area of cooperative driving deals with maneuver-planning algorithms that enable vehicles to behave cooperatively in interactive traffic scenarios. To prove the functionality of these algorithms, single test scenarios are used in the current body of literature. The use of a single, exemplary scenario bears the risk that the presented approach only works in the presented scenario and thus no general statement can be made about the performance of the algorithm. Furthermore, there is a risk that fictitious traffic scenarios may be solved which do not occur in reality. Therefore, we present a procedure for generating test scenarios based on real-world traffic datasets that require cooperation of at least one of the involved vehicles and thus are challenging from the perspective of cooperation. This procedure is applied to a large highway traffic dataset, resulting in a test scenario catalog that allows a comprehensive performance evaluation. The extracted scenarios are clustered according to the cooperative actions used to solve the respective scenario, which enables a more detailed understanding of the underlying cooperative mechanisms. In order to serve as a basis for making comparisons between different behavior planners and thus contribute to the development of future maneuver planning algorithms, a tool to extract the test scenarios from the used traffic dataset is made publicly available. [ABSTRACT FROM AUTHOR]

A simultaneous trajectory tracking and stability control method is present for the four-wheel independent drive (4WID) automated vehicles to handle dynamic coupling maneuvers. To conquer the disadvantage that attendant disturbances caused by the dynamic coupling of traditional decentralized control methods degenerate the trajectory tracking accuracy, the proposed method takes advantage of the idea of decoupling to optimize the tracking performance. After establishing the dynamic model of the 4WID automated vehicles, the coupling mechanism of the vehicle dynamic control and its negative effect on trajectory tracking were studied at first. The inverse system model was then determined by machine learning and connected in series with the controlled object to form a pseudo linear system to realize dynamic decoupling. Finally, differing from previous tracking methods following the apparent lateral position and longitudinal velocity references, the pseudo linear system tracks the ideal intermediate targets transferred from the target trajectory, that is, the accelerations of vehicle in longitudinal, lateral and yaw directions, to indirectly achieve trajectory tracking and validly restrain the vehicle motion. The effectiveness of the proposed method, i.e., the high tracking accuracy and the stable driving performance, is verified through three coupling driving scenarios in the CarSim-Simulink co-simulations platform. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency. Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm. [ABSTRACT FROM AUTHOR]