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Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate (qQ). The results indicated an increase in qQ as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates. [ABSTRACT FROM AUTHOR]

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]

• A cycling simulator was used to recreate the cyclists' interaction scenarios with motorized vehicles. • The effect of the difference in time to arrival at the intersection and visibility distance was observed on the interaction. • Cyclists' behavioral patterns were investigated in response to independent variables. • Eye contact and communication with the driver plays an important role in cyclists decision-making. Introduction: With cycling gaining more popularity in urban areas, it is vital to obtain accurate knowledge of cyclists' behavior to develop behavioral models that can predict the cyclist's intent. Most conflicts between cyclists and vehicles happen at crossings where the road users share the path, especially at unsignalized intersections. However, few studies have investigated and modeled the interaction between cyclists and vehicles at unsignalized intersections. Method: A bike simulator experiment was conducted to scrutinize cyclists' response process as they interacted with a passenger car at an unsignalized intersection. An existing unsignalized intersection in Gothenburg was simulated for test participants. Two independent variables were varied across trials: the difference in time to arrival at the intersection (DTA) and intersection visibility (IV). Subjective and quantitative data were analyzed to model the cyclists' behavior. Results: When approaching the intersection, cyclists showed a clear sequence of actions (pedaling, braking, and head turning). The distance from the intersection at which cyclists started braking was significantly affected by the two independent variables. It was also found that DTA, looking duration, and pedaling behavior significantly affected cyclists' decisions to yield. Finally, the questionnaire outputs show that participants missed eye contact or communication with the motorized vehicle. Conclusions: The kinematic interaction between cyclists and vehicles, along with the cyclist's response process (visual and kinematic), can be utilized to predict cyclists' yielding decision at intersections. From the infrastructural perspective, enhancing visibility at intersections has the potential to reduce the severity of interactions between cyclists and vehicles. The analysis of the questionnaire emphasizes the significance of visual communication between cyclists and drivers to support the cyclist's decision-making process when yielding. Practical applications: The models can be used in threat assessment algorithms so that active safety systems and automated vehicles can react safely to the presence of cyclists in conflict scenarios. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have limited computational resources and thus require efficient control systems. The cost and size of sensors have limited the development of self-driving cars. To overcome these restrictions, this study proposes an efficient framework for the operation of vision-based automatic vehicles; the framework requires only a monocular camera and a few inexpensive radars. The proposed algorithm comprises a multi-task UNet (MTUNet) network for extracting image features and constrained iterative linear quadratic regulator (CILQR) and vision predictive control (VPC) modules for rapid motion planning and control. MTUNet is designed to simultaneously solve lane line segmentation, the ego vehicle's heading angle regression, road type classification, and traffic object detection tasks at approximately 40 FPS for 228 × 228 pixel RGB input images. The CILQR controllers then use the MTUNet outputs and radar data as inputs to produce driving commands for lateral and longitudinal vehicle guidance within only 1 ms. In particular, the VPC algorithm is included to reduce steering command latency to below actuator latency, preventing performance degradation during tight turns. The VPC algorithm uses road curvature data from MTUNet to estimate the appropriate correction for the current steering angle at a look-ahead point to adjust the turning amount. The inclusion of the VPC algorithm in a VPC-CILQR controller leads to higher performance on curvy roads than the use of CILQR alone. Our experiments demonstrate that the proposed autonomous driving system, which does not require high-definition maps, can be applied in current autonomous vehicles. [ABSTRACT FROM AUTHOR]

Introduction: Augmented reality (AR) has been increasingly studied in transportation, particularly for drivers and pedestrians interacting with automated vehicles (AVs). Previous research evaluated AR interfaces using online video-based questionnaires but lacked human-subject research in immersive environments. This study examined if prior online evaluations of nine AR interfaces could be replicated in an immersive virtual environment and if AR interface effectiveness depends on pedestrian attention allocation. Methods: Thirty participants completed 120 trials in a CAVE-based simulator with yielding and non-yielding AVs, rating the interface's intuitiveness and crossing the road when they felt safe. To emulate visual distraction, participants had to look into an attention-attractor circle that disappeared 1 s after the interface appeared. Results: The results showed that intuitiveness ratings from the current CAVEbased study and the previous online study correlated strongly (r = 0.90). Headlocked interfaces and familiar designs (augmented traffic lights, zebra crossing) yielded higher intuitiveness ratings and quicker crossing initiations than vehiclelocked interfaces. Vehicle-locked interfaces were less effective when the attention-attractor was on the environment's opposite side, while headlocked interfaces were relatively unaffected by attention-attractor position. Discussion: In conclusion, this 'AR in VR' study shows strong congruence between intuitiveness ratings in a CAVE-based study and online research, and demonstrates the importance of interface placement in relation to user gaze direction. [ABSTRACT FROM AUTHOR]

The progress of technology in our current world continues to advance each day, benefiting human beings in various ways. One significant development in recent time is the emergency of automated vehicles, which have the potential to revolutionise transportation. These vehicles utilise electric power, sensors, cameras and sound navigators to carry out their intended operations without causing environmental pollution. Currently, there are several autonomous companies, primarily located in California, cities like San Francisco (Cruise), Palo Alto (Tesla), Fremont (Pony.ai), Santa Monica (Motional), Mountain View (Waymo), and Foster city (Zoox). This paper aims to review the utilisation level and performance of autonomous vehicles, specially focusing on the goals set for 2023. By analysing various research studies and company profiles, this paper aims to provide insights into the current status of autonomous vehicles and their practical applications. It employs quantitative and statistical methods to extract valuable information from these studies. Also, this paper examines the state of the art in autonomous vehicles and the impact of gaps in machine learning algorithms, from perception to execution. The data used for this study are obtained from research reviews and updated profile of different companies. The assessment reveals a significant increase in research and development activities related to autonomous and automated vehicles across various disciplines since 2010. Specifically, the number of research studies on autonomous driving vehicles has increased from 302 to 2718, while studies on automated vehicles have increased from 1379 to 6085. In the Engineering discipline alone, there have been 601 studies on autonomous driving vehicles and 341 studies on automated vehicle-related research, which have increased to 2685 and 1865, respectively in the specified time.

This article presents a methodology to estimate the entry capacity (EC) and total capacity (TC) of basic turbo roundabouts under partial and fully connected and autonomous vehicle (CAV) environments. EC calculations are partially based on capacity models and adjustment factors proposed by the HCM 7th edition, taking into account different proportions of CAVs in traffic streams. The proposed methodology was applied to a case study concerning a basic turbo roundabout with different traffic demands and market penetration levels (MPLs) of CAVs. It was assumed that the traffic stream consisted of 100% passenger cars with MPLs of CAVs ranging from 0% to 100%. The research proves that with the increase in MPLs of CAVs, ECs increase accordingly and delays and queues decrease. To maximize the TC, a control area was also hypothesized, where CAVs start to communicate with a turbo roundabout manager system. The system should be able to distribute and channel CAVs, and therefore the entering flows between entry lanes find the values of the maneuver distribution factors (α, β, γ, δ) between the right lane and the left lane of entries to maximize the TC for each origin–destination matrix of traffic flows.

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form. [ABSTRACT FROM AUTHOR]

Vehicle‐based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather‐related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition‐specific applications and winter road maintenance to urban and street‐level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized. [ABSTRACT FROM AUTHOR]

The implementation of the research results is seen as a crucial step in the development of innovation in the transport sector. Moving to such an implementation is not always easy or straightforward. It requires a suitable organizational framework both inside as well as outside research producing entities and a number of other facilitating factors that are usually found within an innovation ecosystem. The paper examines systematically the conditions and prevailing practices for transport research implementation in Europe (the European Union) and China and draws useful insights as to the factors that influence such implementation, the incentives, and other facilitating provisions that the research funding organizations can take. It also analyses the current practice and lessons learned for research implementation on the road to innovation production in four major areas of transport research namely: Automated Mobility, Intelligent Railways, Shared and Micromobility applications, and Electromobility. [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]

The emergence of automated vehicles promises a revolution in urban mobility. To benefit from a new mobility system, women who have specific mobility considerations necessitate inclusion in designing automated vehicles. This study explores women’s perspectives and the potential impact of automated vehicles through focus group discussions and in-depth interviews. Results demonstrate concerns among women about safety in current mobility systems, vulnerabilities regarding personal safety, and stereotypes about female drivers. Additionally, mothers face additional challenges managing items for children and their demands during travel, and senior women consider safety issues and declining capabilities when contemplating driving cessation. Current experience with mobility is reflected in concerns and visions regarding automated vehicles. The absence of a driver is expressed as improved safety in driverless taxis, while it is perceived as a safety concern in automated public transportation. Mothers with children anticipate convenience in travel, whereas senior women expect enhanced mobility and social participation. These findings underscore the importance of safety in women’s mobility experiences and provide insights into addressing safety and interaction issues in the design of automated vehicles. Researchers, transportation authorities, and vehicle manufacturers can leverage these results to understand women’s needs better and consider them in future designs and policy developments for automated vehicles. Prioritising women’s perspectives in automated vehicle research is essential to realising the innovative potential of this technology and fostering a more inclusive and accessible future in urban mobility.

Automated vehicles (AVs) hold the potential to reduce road accidents, mitigate traffic congestion, and improve travel experience. However, the possible countervailing impacts from the changes in underserved populations' vehicle travel demand tend to be overlooked. To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80, this paper explores the latent effect of AVs on vehicle kilometers traveled (VKT) in a fully AV environment using person trip survey data from the cities of Sanya, Shijiazhuang, and Shenzhen in China. This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities' latent vehicle travel demand. Results show that the average VKT of the overall population in Sanya, Shijiazhuang, and Shenzhen increased by 33.4%, 47.0%, and 46.8%, respectively. The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT. Integrating AVs into public transport, increasing the acceptance of automated shared mobility options, transforming road space use type, and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts. The total VKT of the overall population increased by approximately 10%–25% depending on the city. The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues.

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles (AVs). The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer. To address this issue, this study proposes a safe motion planning and control (SMPAC) framework for AVs. For the control layer, a dynamic model including multi-dimensional uncertainties is established. A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set. A flexible tube with varying cross-sections is constructed to reduce the controller conservatism. For the planning layer, a concept of safety sets, representing the geometric boundaries of the ego vehicle and obstacles under uncertainties, is proposed. The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories. An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles. A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC. The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

ABSTRACTIt is often assumed, at least implicitly, that responsible governance of automated vehicles (AVs) requires more knowledge about the future development of the innovation and its potential consequences. In this context, technology assessment (TA) studies often refer to the so-called Collingridge-dilemma. This paper argues that, at least in the German case, a lack of knowledge in the sense of the Collingridge-dilemma is not the central challenge for the governance of AVs. The argument is developed on the basis of different types of knowledge for TA recently introduced by Armin Grunwald. The paper shows that responsible governance of AVs requires more normative and hermeneutic knowledge to better understand the directionality of the current system. More important than focusing on the possible consequences of AVs is a better understanding of how to overcome existing obstacles to the development of a broadly shared vision with effective goals for the German mobility sector.

The second decade of the 21st century saw the introduction of vehicles equipped with various driver assistance systems. However, the producers' plans go much further. In January 2023, Mercedes-Benz's DRIVE PILOT system was approved for use on public roads in Nevada (United States) as the first and so far only SAE Level 3 seriesproduction solution. Despite increasing automation, the human factor remains important for the safety and efficiency of road transport. In the coming years, and possibly decades, this will mainly be due to the co-existence of vehicles with varying degrees of automation and necessary driver-vehicle interactions in cases where an automated driving system crosses the boundaries of the operational design domain. The latest research reveals the limitations of driving assistance systems, pointing not only to the need for changes in the technological domain but also to the need to pay more attention to the psychological aspects of using assistance systems in cognitive and behavioral areas. Therefore, an additional element of automation implementation should be considered, which is driver training focused on modern technological solutions. Properly arranged and conducted training can be treated as an additional factor facilitating the implementation of automated mobility. The article describes the methods of assessing the effectiveness of training in terms of familiarizing drivers with the functions that automate driving. The authors present the results of tests carried out on a driving simulator with an implemented Level 4 system (according to SAE classification). Three forms of knowledge transfer were analyzed: practical training, e-learning, and training with a short user manual. The results were compared in terms of driver-vehicle interaction effectiveness (e.g., system activation), ensuring that the driver is sufficiently reactive in dangerous situations and understands the limitations of the system. The research was conducted on a group of 81 drivers aged 18-65. Driver training was one of the main pillars of the Trustonomy project, which received funding from the EU research and innovation program Horizon 2020 under grant agreement No. 815003. [ABSTRACT FROM AUTHOR]

Self-driving car companies aim to offer hands-off journeys with Automated Vehicle (AV) technology, but public skepticism persists on safety aspects of AVs. A proposed solution involves creating dedicated AV corridors with traffic control and interruptions. Realistically, full penetration of AVs is unlikely to occur in the near future while mixed traffic of AVs with human driven vehicles is more likely. This study applies this concept to a hypothetical scenario in NYC, proposing an AV corridor from the Staten Island Expressway to the Brooklyn Battery Tunnel. The goal is to assess the network-wide impacts of a limited AV deployment from a multi-modal perspective. The NYC transportation system is modeled using MATSim, an open-source agent-based modeling platform. Although each traveler is treated as an agent making multi-modal trip making decisions, it does not model microscopic behavior of cars. Thus, the modeling approach for AVs has to be a macroscopic one, where average headways are reduced on the AV (with mixed traffic) corridor portion of the overall network based on a link capacity relationship adopted from a study on Advanced Cruise-Assist Highway Systems [1]. Two reduced headway scenarios are simulated in this MATSim NYC model, 1.1 and.85 second headways. Results indicate that Scenario B (1.1-second headway) is preferable over Scenario A (0.85 second headway), mitigating potential congestion on connectors, especially on Victory Boulevard in Staten Island. This study suggests implementing Scenario B for a smoother corridor-wide introduction of AVs to avoid congestion on connectors of the AV corridor to the rest of the NYC network. [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]

Objective: This study investigated drivers' subjective feelings and decision making in mixed traffic by quantifying driver's driving style and type of interaction. Background: Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method: Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers' subjective feelings and decision making were collected via questionnaires. Results: Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion: Driving style and type of interaction significantly influenced drivers' subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application: This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience. [ABSTRACT FROM AUTHOR]

Abstract Vehicle‐based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather‐related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition‐specific applications and winter road maintenance to urban and street‐level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized.

Introduction: Augmented reality (AR) has been increasingly studied in transportation, particularly for drivers and pedestrians interacting with automated vehicles (AVs). Previous research evaluated AR interfaces using online video-based questionnaires but lacked human-subject research in immersive environments. This study examined if prior online evaluations of nine AR interfaces could be replicated in an immersive virtual environment and if AR interface effectiveness depends on pedestrian attention allocation.Methods: Thirty participants completed 120 trials in a CAVE-based simulator with yielding and non-yielding AVs, rating the interface’s intuitiveness and crossing the road when they felt safe. To emulate visual distraction, participants had to look into an attention-attractor circle that disappeared 1 s after the interface appeared.Results: The results showed that intuitiveness ratings from the current CAVE-based study and the previous online study correlated strongly (r ≈ 0.90). Head-locked interfaces and familiar designs (augmented traffic lights, zebra crossing) yielded higher intuitiveness ratings and quicker crossing initiations than vehicle-locked interfaces. Vehicle-locked interfaces were less effective when the attention-attractor was on the environment’s opposite side, while head-locked interfaces were relatively unaffected by attention-attractor position.Discussion: In conclusion, this ‘AR in VR’ study shows strong congruence between intuitiveness ratings in a CAVE-based study and online research, and demonstrates the importance of interface placement in relation to user gaze direction.

We present a computational study of network ensembles with two types of coexisting vehicle classes: an altruistically routing vehicle (ARV) class – potentially automated vehicles that are routed to reduce total system travel time – and a selfishly routing vehicle (SRV) class, corresponding to human-driven vehicles. We investigate the performance of these networks when some links are reserved for exclusive use by the ARVs. The goal of these interventions is to avoid or mitigate the detrimental effects of the SRVs on the costs of the ARVs. We formulate the problem as a bi-level network design problem, where the upper level deals with optimising the choice of ARV-exclusive links minimising the statistical dispersion of used-route costs, while the lower level finds the corresponding traffic equilibrium under static traffic assignment conditions. We tackle the ARV-exclusive link selection with a genetic algorithm, where the fitness of solutions is based on the dispersion of the costs of routes used by ARVs. The mixed equilibrium is found by solving a multi-class static traffic assignment problem, with constraints on the SRV flows on the ARV-exclusive links. SRVs attempt to minimise their personal travel time, whilst ARVs attempt to drive the flows to system optimal. Our approach is effective in reducing the per-vehicle travel cost of the ARVs to below that of the SRVs, making altruistic routing a more attractive option on average. Our results are consistent across networks with different structures and demand levels.

Comprehensive and accurate understanding of the interactive traffic environment facilitates reasonable motion planning for automated vehicles. This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted driving behaviors of surrounding vehicles. A Social Temporal Convolutional Long Short-Term Memory network is constructed to capture the interactive characteristics among the host and surrounding vehicles and to predict the statistical distribution of the trajectory prediction uncertainty in the prediction horizon. Then a two-dimensional Gaussian distribution-based dynamic risk assessment with a soft update method is developed to spatially and temporally quantify the driving risk by constructing the occupancy map based on the multi-modal distribution of the predicted trajectories for the surrounding vehicles. The optimal motion of the host vehicle is determined by minimizing a multi-objective function of the alternative driving behaviors. The effectiveness of the proposed scheme is verified under typical driving scenarios extracted from the NGSIM dataset. The results show that the proposed method can comprehensively evaluate the potential risk and efficiently achieve motion planning while minimizing the driving risk.

Achieving robust path tracking is essential for efficiently operating autonomous driving systems, particularly in unpredictable environments. This paper introduces a novel path-tracking control methodology utilizing a variable second-order Sliding Mode Control (SMC) approach. The proposed control strategy addresses the challenges posed by uncertainties and disturbances by reconfiguring and expanding the state-space matrix of a kinematic bicycle model guaranteeing Lyapunov stability and convergence of the system. A state prediction is integrated into the developed SMC to mitigate response time delays. Furthermore, the controller integrates adaptive mechanisms to adjust time-varying parameters within the control formulation based on longitudinal velocity, thereby enhancing path-tracking performance and reducing chattering phenomena. The effectiveness of the proposed approach is comprehensively evaluated through simulations and experiments encompassing challenging driving scenarios characterized by high-curvature paths, varying altitudes, and sensor disturbances, typical in rural driving environments. Results demonstrate that disturbances have varying impacts depending on the type of sensor affected. Real-world tests validate these findings, offering practical insights for automated vehicle path-tracking implementation.

This paper introduces a novel mmWave joint radar and communication (JRC) system operating in the 24 GHz ISM band, specifically designed for automated connected vehicles. The system utilizes a single transceiver platform featuring a frequency-shift keying modulation scheme that enables simultaneous operation in the radar and communication modes. The proposed JRC system boasts several distinctive features. Firstly, its simultaneous sensing and communication capability, a departure from the conventional sequential arrangement in the time domain. Secondly, the system is characterized by its simplicity, offering ease of implementation and operation. The third feature addresses synchronization challenges through an innovative approach that eliminates the dependence on a shared clock or timing reference, enhancing the system flexibility and adaptability. Lastly, the system stands out for its cost-effectiveness. Results from simulations and experiments collectively showcase the system’s promising performance. In particular, the JRC system demonstrates robust radio communication capabilities with adjustable data rate based on the instantaneous measured Doppler frequency, while also exhibiting promising radar capabilities. This dual functionality underscores the system’s versatility and potential for practical applications in smart environments. Notably, throughout the transmission of vital information, no errors were detected in the communication mode, affirming the reliability of the system.

Accurate distance estimation is crucial for ensuring the safety and efficiency of intelligent and future autonomous mobility. In this paper, we propose a method for real-time estimation of visible lane distance based on image data only from a single camera and an existing road lane marking estimation system. By leveraging camera intrinsic and height above the road, we utilize lane parallelism and geometric properties of frames from a camera to compute the distance to the furthest visible lane point. Intelligent recalculations of horizon level are performed for every frame, accommodating variations encountered during uphill or downhill driving scenarios. Moreover, the proposed solution is designed to be theoretically adaptable to gauge the distance to any detected object on the camera frame, not only road lanes. We evaluate the effectiveness of our method on a dataset created for this purpose, assessing estimation performance through comparison with ground truth data obtained from high-precision GPS measurements. The results indicate comparable estimation performance with other works. Coupled with the capability for real-time implementation in vehicles, we underscore the potential of our approach for advanced driver assistance systems (ADAS) in various aspects, such as safe driving speeds, obstacle avoidance, or collision prevention. This research contributes to the ongoing efforts in developing robust and practical solutions for autonomous mobility applications.

Automated and connected vehicles are emerging in the market. Currently, solutions are being proposed to use these technologies for cooperative driving, which can significantly improve road safety. Vehicular safety applications must be tested before deployment. It is challenging to verify and validate them in the real world. Therefore, simulation is used for this purpose. Modeling these technologies necessitates coupled the use of traffic flow, vehicle dynamics, communication network, perception and signal propagation models. State-of-the-art tools exist in these domains; however, they lack full domain coverage, and the low-level processes that affect each vehicle’s behavior are often left unaccounted for, which (according to complex system theory) can lead to erroneous assumptions about system-level outcomes. This paper analyzes the requirements for an integrated connected and automated vehicle simulation environment for simulating vehicle behavior with consideration of surrounding objects’ influence on machine perception and signal propagation. We discuss the shortcomings of existing methods, propose an architecture for a simulation environment with full domain coverage, and develop a preliminary version of the CAVISE integrated simulation environment for connected and automated vehicles using open-source tools. Scenarios of object detection and signal exchange were simulated, and data on the influence of surrounding objects on quantitative and qualitative changes in scenario simulation results was obtained. It was found that a more accurate description of surrounding objects and their influence on machine perception and signal transmission does lead to quantitative and qualitative changes in the measured parameters. This conclusion should be further used in simulation environment development for connected and automated vehicle technology verification and validation.

The safety approval of Highly Automated Vehicles (HAV) still faces the automotive industry with economic and legal challenges. For verification and validation, it is essential to describe the intended behavior of an HAV in the development process in order to prove safety. The demand for this behavior comes from the traffic rules which are instantiated by the present scenery around the vehicle (e.g. traffic signs or road markings). The Operational Design Domain (ODD) specifies the scenery in which an HAV may operate, but current descriptions fail to explicitly represent the associated behavioral demand of the scenery. We propose a new approach for a Behavior-Semantic Scenery Description (BSSD) in order to describe the behavior space of a present scenery. A behavior space represents the delimitation of the legally possible behavior. The BSSD explicitly links the scenery with the behavioral demand for HAV. Based on identified goals and challenges for such an approach, we derive requirements for a generic structure of the description for complete road networks. All required elements to represent the behavior space of the scenery are identified. Within real world examples, we present an instance of the BSSD integrated into the HD-map framework Lanelet2 to prove the applicability of the description. The presented approach supports development, test and operation of HAV by closing the knowledge gap of where a vehicle has to behave in which limits within an ODD.

Several past studies showed that Autonomous Vehicles (AVs) can reduce crash risk, stop-and-go traffic, and travel time. To analyze the safety benefits of AVs, most of the researchers proposed algorithms and simulation-based techniques. However, these studies have not assessed the safety benefits of AVs for different vehicle types under heterogeneous conditions. With this opportunity, this study focuses on the benefits of AVs in terms of safety for different penetration rates under heterogeneous conditions. This study considered three driving logics during peak hour conditions to assess the performance of AVs in terms of safety. In VISSIM, default driving behavior models for AVs were adopted to consider cautious and all-knowing driving logic and the third driving logic (Atkins) was modeled in VISSIM using parameters adopted from the previous studies. To this end, using VISSIM, the travel time output results were obtained. Also, using Surrogate Safety Assessment Model (SSAM), conflicts were extracted from output trajectory files (VISSIM). The results suggest that “cautious driving logic” reduced travel time and crash risk significantly when compared to the other two driving logics during peak hour conditions. Furthermore, the statistical analysis clearly demonstrated that “cautious driving logic” differs significantly from the other two driving logics. When Market Penetration Rates (MPR) were 50% or greater, the “cautious driving logic” significantly outperforms the other two driving logics. The results highlight that adopting “cautious driving logic” at an expressway may significantly increase safety at higher AV penetration rates (above 50%).

The present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and aggregate questions pertaining to four main pillars of the questionnaire, which are conceptually relevant namely: (i) how several factors affect general car choices of respondents, (ii) what the respondents perceived that AVs would offer, (iii) how much they agreed with stated expected technology and efficiency-oriented AV traits and (iv) how they believe several factors affect driving behavior overall. A Random Forest (RF) algorithm was applied to classify the AV acceptance decisions of a training subset of the respondents, and was subsequently assessed on a test subset. SOM results indicate that participants can be meaningfully separated into two SOM cluster groups for pillars (i), (ii) and (iv), while pillar (iii) yielded separations into three SOM cluster groups. RF feature importance calculation indicated a number of affecting variables; the five most contributing ones are: distance covering capabilities of AVs was a major factor affecting acceptance decisions, followed (by a wide margin) by responder opinions on whether the principles and conscience of drivers can be replaced by an AI navigator without reducing safety levels, while the algorithm itself conducted successful classification to about 80% of test cases. Present results can be used to anticipate AV penetration levels based on sample characteristics and to improve AV traits in cases where higher AV penetration is sought.

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]

Autonomous Vehicles (AVs) have the potential to (re)shape the urban transportation network drastically. The significant investment by the automotive industry and leading technology companies in AVs has resulted in a substantial surge in the number of published documents in this domain. Therefore, this study aims to analyze the current state and trajectory of AVs research by conducting a comprehensive review of the available literature. The study employs scientometric methods to examine the scientific landscape of AVs and assess the position of Urban Transportation Planning (UTP) within this context. The analysis encompasses both a macro-level perspective and a meso-level focus on UTP, utilizing datasets of journal articles published up to January 2023. The study addresses various questions such as identifying the main research trends, evaluating the impact and influence of countries and sources, determining the collaboration level among different countries, and assessing the maturity of AVs domain in the field of UTP. To accomplish this, the study analyzes the conceptual, intellectual, and social landscapes of AVs from both a holistic and macro-level perspective, as well as from the UTP perspective in a meso-level. The findings highlight a significant disparity between attention on AVs' UTP aspect and their technical advancement, emphasizing the need for more comprehensive research to fully comprehend the implications of AVs deployment from the UTP perspective. The comprehensive understanding of the literature gained from this study will enable scholars to identify research gaps, necessities, and potential avenues for future research. [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]

There is a growing body of research on trust in driving automation systems. In this paper, we seek to clarify the way trust is conceptualized, calibrated and measured taking into account issues related to specific levels of driving automation. We find that: (1) experience plays a vital role in trust calibration; (2) experience should be measured not just in terms of distance traveled, but in terms of the range of situations encountered; (3) system malfunctions and recovery from such malfunctions is a fundamental part of this experience. We summarize our findings in a framework describing the dynamics of trust calibration. We observe that methods used to quantify trust often lack objectivity, reliability, and validity, and propose a set of recommendations for researchers seeking to select suitable trust measures for their studies. In conclusion, we argue that the safe deployment of current and future automated vehicles depends on drivers developing appropriate levels of trust. Given the potentially severe consequences of miscalibrated trust, it is essential that drivers incorporate the possibility of new and unexpected driving situations in their mental models of system capabilities. It is vitally important that we develop methods that contribute to this goal. [ABSTRACT FROM AUTHOR]

Objective: Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background: Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method: Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results: Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants' distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion: The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application: Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers' situation awareness. Précis: This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews. [ABSTRACT FROM AUTHOR]

The present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and aggregate questions pertaining to four main pillars of the questionnaire, which are conceptually relevant namely: (i) how several factors affect general car choices of respondents, (ii) what the respondents perceived that AVs would offer, (iii) how much they agreed with stated expected technology and efficiency-oriented AV traits and (iv) how they believe several factors affect driving behavior overall. A Random Forest (RF) algorithm was applied to classify the AV acceptance decisions of a training subset of the respondents, and was subsequently assessed on a test subset. SOM results indicate that participants can be meaningfully separated into two SOM cluster groups for pillars (i), (ii) and (iv), while pillar (iii) yielded separations into three SOM cluster groups. RF feature importance calculation indicated a number of affecting variables; the five most contributing ones are: distance covering capabilities of AVs was a major factor affecting acceptance decisions, followed (by a wide margin) by responder opinions on whether the principles and conscience of drivers can be replaced by an AI navigator without reducing safety levels, while the algorithm itself conducted successful classification to about 80% of test cases. Present results can be used to anticipate AV penetration levels based on sample characteristics and to improve AV traits in cases where higher AV penetration is sought. • This study analyzes 563 questionnaire responses on AV acceptance. • Self-Organizing Maps are employed for 4 question groups. • The sample is separated into two SOM clusters for most groups. • Random Forest determines distance covering capabilities as critical for AV acceptance. • Random Forest achieves 80% predictive accuracy in test data. [ABSTRACT FROM AUTHOR]

The driving style of an automated vehicle (AV) needs to be comfortable to encourage the broad acceptance and use of this newly emerging transport mode. However, current research provides limited knowledge about what influences comfort, how this concept is described, and how it is measured. This knowledge is especially lacking when comfort is linked to the AV’s driving styles. This paper presents results from an online workshop with nine experts, all with hands-on experience of AVs and a long track record of research in this context. Using online tools, experts were invited to introduce concepts they considered relevant to comfort/discomfort in currently available modes of transport which offer a ride (taxi/bus/train) to users and compare these to the concepts used to define comfort and discomfort in AVs. Results showed that a wide range of terms were used to describe user comfort and discomfort for both modes. Although all terms used for existing vehicles were found to apply to AVs, additional terms were proposed for determining comfort/discomfort of AVs. For example, to enhance comfort in AVs, designers should consider good communication channels, as well as ensuring that the AV’s capabilities match users’ expectations. Results also revealed that more terms were used, overall, to define discomfort, and that a comfortable ride in AVs is not just about mitigating discomfort. New concepts specific to AVs were also revealed when considering what increases their discomfort, such as whether riders’ safety and privacy are affected, or if they feel in control. Experts’ input from the workshop was used to enhance and expand a simple conceptual framework, explaining how AV driving styles, as well as other, non-driving-related factors, affect user comfort. It is hoped that this framework provides a more comprehensive list of the concepts affecting user comfort, also allowing more accurate measurement of the concept. As well as allowing for a more accurate comparison between empirical studies measuring comfort in AVs, this study will facilitate the design of more comfortable and acceptable automated driving for future vehicles.

Roadway departure (RwD) crashes are significant safety concerns, especially at horizontal curves. The design of these curves plays a crucial role in mitigating RwD crashes. Thus, a thorough understanding of the interaction between driver behavior, vehicle automation, and geometric design is vital. Substantive safety, which emphasizes the inherent safety in a road's design and function, serves as the foundation of our approach. Building on this, the study employs a safe system approach to investigate the performance of horizontal curves under both non-automated and partially automated conditions, using a reliability-based analysis focusing on Stopping Sight Distance as the primary driver demand. Factors including Perception-Brake Time and Take-Over Time for automated vehicles are examined. The analysis covers horizontal curves, characterized by their geometric design and crash data. Our findings highlight a shift in the performance of horizontal curves under automation, emphasizing the need to consider automation in roadway design within the safe system approach. This study demonstrates how a reliability-based analysis can guide designers in making informed decisions regarding the geometric design of horizontal curves to reduce RwD crashes. To enhance transportation safety in the era of increasing automation, ongoing exploration of the relationships between driver behavior, automation, and road design is indispensable.