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Adaptive cruise control (ACC) is an important part of the advanced driver assistance system (ADAS), which aims to improve the user’s driving experience by reducing their manual operation and optimizing the vehicles’ performance such as economy, comfort, and safety. With the gradual maturity of research on ACC, it presents various development trends. Among them, ecological adaptive cruise control (EACC) has great development potential in reducing emissions and improving driving range in internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and electric vehicles (EVs). This paper firstly reviews the components of classic ACC. Then the main design methods, research hotspots, and development trends of EACC for HEVs and EVs are introduced in detail. This paper not only provides a comprehensive analysis of EACC design and application in different type of vehicles but also forecasts the future research direction and trend, expecting to promote the further development of EACC efficiently.

Logs of vehicle controller area network (CAN) bus traffic supplemented by accelerometer and GPS data can provide valuable information about the use and operation of advanced driver assistance systems (ADAS) to the broader safety research community. Although CAN bus message codes are often manufacturer-specific, third-party libraries provide partial decoding of messages from many vehicle models, which can be augmented by reverse-engineering additional signals. This study explored the value of CAN bus, accelerometer, and GPS data that were logged on a variety of light vehicle models with an emphasis on availability of lane keep assist/lane departure warning, and adaptive cruise control. This study demonstrated that in-vehicle ADAS variables such as system on/off status and whether the system is actively controlling the vehicle could be determined on a variety of vehicle types. Associated control variables such as steering wheel angle, gas pedal state, and brake pedal state could also be determined from most vehicles tested. CAN messages, together with roadway features identified via GPS location, can provide a richer understanding of ADAS efficacy. Comparisons of message structure between models may also inform standardization efforts for electronic data recorders and telematics.

Contemporary transportation is developing toward applications of large-capacity vehicles. This trend is caused mainly by environmental and economic factors. In the public transportation area, it leads to the use of long and articulated electric buses, which are difficult to maneuver, even for experienced drivers. Consequently, there is a need to develop advanced driver-assistance systems (ADAS) for operators of urban buses to safely perform positioning maneuvers with acceptable precision. This article presents an ADAS concept dedicated to the task of precisely docking a pantograph tip with a charging station. We describe the functional structure of the ADAS and explain details of its interconnected components responsible for motion algorithmization, environmental perception, and vehicle localization. Experimental results of assisted pantograph docking maneuvers obtained with an articulated electric bus are provided.

Radar is a key sensor to achieve a reliable environment perception for advanced driver assistance system and automated driving (ADAS/AD) functions. Reducing the development efforts for ADAS functio...

Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies.

Motor vehicle crashes claimed 38,800 lives and caused 4.4 million injuries in 2019 alone. Studies have shown that 94% of these crashes are because of driver errors. Such a huge contribution of driver errors to crashes indicates that efforts at improving safety should be directed toward both vehicles and drivers through advanced driver assistance systems (ADAS) and vehicular technologies. This study investigates the potential that real-time driver behavior data collected through vehicular technologies offer to predict crashes, as the first line of defense to avoid them. Three deep learning models were developed including multilayer perceptron neural networks (MLP-NN), long-short-term memory networks (LSTMN), and convolutional neural networks (CNN) using vehicle kinematics time series data extracted from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) dataset. The study builds on the hypothesis that crashes are preceded by turbulences that take place over time (turbulence horizon). If these turbulences are detected promptly they can help predict and avoid crashes. Several values were tested for the turbulence horizon and the prediction horizon (how long before the crash impact it can be predicted) to identify the optimal values. The results showed that the CNN model can predict all crashes with a 100% accuracy and zero false alarms 3 s before the crash impact time when a 6-s turbulence horizon is used. This outstanding performance demonstrates the developed model is a promising tool for implementation in ADAS.

This paper reports on the results of an evaluation of a rear-end and pedestrian crash warning system installed on transit agency buses with the goal of collecting and providing information to help agencies make decisions about investing in such systems. The results from this evaluation indicate that the tested crash warning system had a positive effect on improving the reaction times to rear-end and pedestrian conflicts and on increasing the yield of drivers to pedestrians. The results from the evaluation also indicate improvement in driver behavior as reflected by the increase in the time headway between vehicles, reduction in the number of alerts for both rear-end and pedestrian crashes, and the reduction in the number of hard brake events. However, bus operators’ acceptance of the system seems to be low, pointing to the need for additional outreach and education of the drivers on the system and its effectiveness. The results from the return-on-investment analysis show that although installing the system on every bus of the transit agency may not be cost-effective, installing the devices on only the buses that operate on the high-crash bus routes is cost-effective.

YOLOv2 is an object detection algorithm grounded on the Darknet neural network, widely applied in the advanced driver assistance system. Nevertheless, the YOLOv2 algorithm must be accelerated on a high-performance computing platform before being put into practical usage. Various computing platforms have their specific features. The merits or drawbacks of the accelerated platform are hard for the developers to recognize and pick up the right alternative based on real demands. This paper analyzes the pros and cons of embedded GPU and FPGA for improving the YOLOv2 algorithm concerning development speed, power efficiency, and computing performance. The analysis provides the developers with insights into choosing the hardware to optimize the YOLOv2 algorithm. According to the experimental data, it is found that if FPGA is optimized profoundly, the performance of power efficiency, as well as speed, will exceed embedded GPU. However, the FPGA development procedure is tough and demands much more time for developers than the GPU development process. Finally, we propose a balanced method to take advantage of GPU’s development speed and FGPA’s high performance.

Understanding and predicting human driving behavior play an important role in the development of intelligent vehicle systems, particularly for Advanced Driver Assistance System (ADAS) to estimate d...

Much of the existing research on drivers’ understanding of adaptive cruise control (ACC), a type of advanced driver assistance system, was conducted several years ago. Through an online survey, this study aimed to assess ACC knowledge among ACC owners and non-owners now that this system is more widely available. Along with knowledge of ACC features and limitations, demographic information, experience with technology, and experience with ACC (for owners) were also collected to investigate which factors predicted knowledge of ACC features and limitations. Results showed that owners today may have a better understanding of some of the main limitations of ACC compared with research conducted over 10 years ago. However, a large percentage of owners still had misperceptions about their ACC system. While owners had a slightly higher percentage of correct answers overall, they did not differ from non-owners in their knowledge of limitations. As this technology is becoming more common, even non-owners may be becoming aware of common limitations; owning and using ACC does not seem to result in a better system understanding. Higher income was associated with a higher percentage of correct responses on the ACC knowledge questionnaire for both owners and non-owners, and for non-owners, higher education level was also significantly associated with a higher percentage of correct responses. Future research should focus on developing training materials that are accessible to all drivers, so that drivers in lower education and income groups are also supported to understand how advanced driver assistance systems work and benefit from these technologies.

Advanced driver assistance and accident detection system is significantlyneeded to ensure safety for drivers. Drowsiness detection, collision detection and various driver alert systems have penetrated into market with an aim to provide higher security for driver but due to population of vehicle and modification in structure of roads these system fails to answer safety problems that results in severe accidents.In this paper we provide accurate analysis of past recorded accidents in Tamil Nadu state and analysis of public opinion on Accident detection system is carried out using 1004 licensed persons under different ages in three cities (Coimbatore, Erode and Nilgiris) by focusing the major 10 parameters carrying 48 Questions. Findings and implications of this analysis is also discussed in this article. A thorough analysis of recent techniques that are used for AAD (Automatic Accident Detection) and road safety programmes that resolve the pre and post cautionary concerns of accidents in developing countries is addressed with the review of most 4 influencing algorithms in ITS for AAD.. 1 Vehicle Detection using Wheel arc Counter Detection Algorithm, 2 Enhancement of V2X Communication using Multi-RAT,3Road Curvature Estimation using Circle Fitting Algorithm and4Driver Safety Systemis discussed in this paper. To understand recent computational challenges and extended areas of research in ITS, anhybrid approach of CNN with VANETs for accident detection has been suggested to enumerate the obtained accidental information.

This study aimed to identify the key factors associated with Australian parents' willingness to use an automated vehicle to transport their unaccompanied child(ren). Seven hundred and seventy-five participants completed the online survey (M=40.7 years, SD=8.9 years, Range=18.0-65.0 years; Female: 56.4%). Most participants reported that they would 'never' use an automated vehicle to transport their unaccompanied child(ren) (43.5%). The results of a logistic regression model showed that participants' age, gender, level of education, propensity for technology adoption, aberrant driving behaviours, awareness of advanced driver assistance safety (ADAS) technologies, perceived knowledge regarding automated vehicles, as well their requirements for assurance-related vehicle features were significantly associated with their willingness to use an automated vehicle to transport their unaccompanied child(ren), χ2(12)=137.41, p

To address the advanced driver assistant system (ADAS) in practical application with some disturbances, we propose a novel finite frequency $H_\infty $ observer-based method in this study, which enables effectively object tracking to restrain the disturbance during middle and low frequency ranges. We first analyze the Radar tracking characteristics. Then, an $H_\infty $ observer is established. Based on the $H_\infty $ theory and Kalman-Yakubovic-Popov (KYP) lemma, the design criterion of the finite frequency $H_{\infty }$ observer is established by a linear matrix inequality (LMI). Lastly, two real world experiments are given to demonstrate the effectiveness of the proposed method.

Older adults are more likely to get severely injured or die in vehicle crashes. Advanced driver-assistance systems (ADAS) can reduce their risk of crashes; however, due to the lack of knowledge and training, usage rate of these systems among older drivers is limited. The objective of this study was to evaluate the impact of two ADAS training approaches (i.e., video-based and demonstration-based training) on older drivers’ subjective and objective measures of mental workload, knowledge and trust considering drivers’ demographic information. Twenty older adults, balanced by gender, participated in a driving simulation study. Results indicated that the video-based training might be more effective for females in reducing their mental workload while driving, whereas the demonstration-based training could be more beneficial for males. There was no significant difference between the video-based and demonstration-based trainings in terms of drivers’ trust and knowledge of automation. The findings suggested that ADAS training protocols can potentially be more effective if they are tailored to specific driver demographics.

To demonstrate the expected performance of an advanced driver assistance system (ADAS) in an intelligent or a highly automated vehicle test approaches should include a combination of simulations, t...

Nowadays, in research introducing an advanced driver assistance system for improving driving is considered as the trending one. In this research, concentrate more on proposing lane detection model ...

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.

Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs., Comment: Paper accepted by IEEE Trans. on Intelligent Transportation Systems. arXiv admin note: substantial text overlap with arXiv:1807.10580

Advanced driver assistance functions help us prevent the human-based accidents and reduce the damage and costs. One of the most important functions is the lane keeping assist which keeps the car safely in its lane by preventing careless lane changes. Therefore, many researches focused on the lane detection using an onboard camera on the car as a cost-effective sensor solution and used conventional computer vision techniques. Even though these techniques provided successful outputs regarding lane detection, they were time-consuming and required hand-crafted stuff in scenario-based parameter tuning. Deep learning-based techniques have been used in lane detection in the last decade. More successful results were obtained with fewer parameter tuning and hand-crafted things. The most popular deep learning method for lane detection is convolutional neural networks (CNN). In this study, some reputed CNN architectures were used as a basis for developing a deep neural network. This network outputs were the lane line coefficients to fit a second order polynomial. In the experiments, the developed network was investigated by comparing the performance of the CNN architectures. The results showed that the deeper architectures with bigger batch size are stronger than the shallow ones.

Ambiguous situations in traffic often require communication and cooperation between road users. In order to resolve these situations and increase cooperative driving behavior in situations of merging or turning left, manual drivers could be assisted by an advanced driver assistance system (ADAS) for cooperative driving. This simulator study investigated the behavior of drivers confronted with system limits and failures of such a system. The ADAS used in this study informed the driver about an upcoming cooperation situation and gave advice on how to behave (e.g. reduce speed, change lane). Two test situations were implemented: a system freeze and an unexpected event, which could not be detected by the system. In order to find the most fitting HMI solution, the place of presentation (head-up display (HUD) vs. instrument cluster) as well as the form of presentation (dynamic vs. symbolic) were varied. The results indicated that the most fitting HMI solution to support the driver in a complex coordinated driving situation is a dynamic HUD, mainly due to the positive effect on glance behavior. However, advantages of both forms of presentation were revealed, as each form of presentation increased the probability of recognition for one of the test situations. The fewest collisions took place with the dynamic form of presentation.

Lane-changing detection is one of the most challenging tasks in advanced driver assistance system (ADAS). However, modeling driver's lane-changing process is challenging due to the complexity and u...

This paper proposes an integrated lane-change trajectory planning method for advanced driver assistance system of connected and automated vehicles. First, the time-based quintic polynomial automated lane-change model is presented, which could adjust longitudinal and lateral velocity simultaneously. By tuning the lane-change duration and longitudinal displacement in the lane-change model, the lane-change reference trajectories satisfying the demands of safety, lane-change duration, travel distance, and comfort were derived under traffic-free condition. All feasible reference trajectories compose a trajectory map, which includes different driving situations, such as quick and comfortable or sudden and safe lane change. Second, the lane-change constraints induced by surrounding vehicles are introduced. The effects of surrounding vehicles on the lane-change performance are investigated by adjusting the speeds and initial gaps of preceding and rear vehicles. In addition, the initial velocity of the host vehicle is optimized to maximize the area of the trajectory map to enable a safer lane change. Finally, within the derived trajectory map, an optimal lane-change trajectory eliminating potential collisions is calculated by minimizing the lane-change duration, travel distance, driving comfort, and fuel consumption.

The introduction of modern and more advanced vehicles has stretched their performance boundaries dramatically in terms of pace and maneuverability. It has also significantly enhanced the likelihood of people losing control of their vehicle, contributing to accidents. Within the past, several strategies have been suggested which resolve this issue by restricting the car's travel to just one specific path. To various road situations, this is achieved by applying lane identification utilizing algorithms such as canny edge identification, Hough transformations, vanishing point estimates, principal component analysis etc. Practical deployment of these programs, however, requires extremely powerful hardware such as TDA3x which can process in real time. This paper aims to introduce the usage of the Lane Detection and Alert System on a Texas Instruments Driver Assist 3x (TDA3x) board with a frame resolution of (1920 * 1080p) at 2 GHz relative to the current implementation, which has a resolution of only (480 * 270p) at 100 MHz [16]. This system too makes use of canny edge detection and hough transforms to identify the lane points, and tracks the vehicle movement by extracting the corresponding polar co-ordinates.

Now Advanced Driver Assistant Systems (ADAS) have entered the consumer market policymakers need instruments to monitor not only the amount of ADAS-equipped cars but also whether car owners actually use these systems; a prerequisite for many ADAS to achieve road safety and/or CO2 reduction benefits. Although questionnaires provide the most common method for large scale fleet assessment, current questionnaires fall short in their ability to assess ADAS usage. Their main pitfall is that ADAS ownership – necessary to compute the gap between ADAS ownership and usage – is based on self-reported rather than actual ADAS ownership. In other words, it is implicitly presumed that car owners are aware of the ADAS with which their car is, or is not, equipped. To our knowledge, the presumption of awareness of ADAS ownership has not been acknowledged as a limitation in current questionnaire-based studies on ADAS usage. Therefore, this paper presents a methodology to address the awareness gap by combining survey and vehicle registration analysis to assess car owners’ usage of ADAS in fleets. In this method, ADAS awareness constitutes of the difference between ADAS presence based on self-report and ADAS presence based on the vehicle specifications of participants’ own car (obtained through participants’ licence plate registration number). To test both the assumption that an awareness gap exists for ADAS as well as the feasibility of the method itself, a survey was performed under 1355 Dutch business drivers. It involved ADAS such as Adaptive Cruise Control, Lane Departure Warning and Emergency Brake. The study showed that many business drivers were unaware of with which ADAS their car is equipped and as a result, they were unable to report they own the ADAS concerned. However, drivers aware of owning a specific system generally reported that they use it. Additionally, the study revealed another group of participants: those who stated owning a specific ADAS while their vehicle specifications contradict this. In conclusion, awareness of ADAS ownership constitutes an important link that is currently missing when concatenating market penetration studies and regular questionnaires on ADAS usage. The proposed method may overcome this shortcoming, increase the validity of surveys on ADAS usage and provide large scale insights in the use of ADAS throughout an entire fleet of vehicles.

Future vehicles will be increasingly connected to enable new applications and improve safety, traffic efficiency and comfort, through the use of several wireless access technologies, ranging from vehicle-to-everything (V2X) connectivity to automotive radar sensing and Internet of Things (IoT) technologies for intra-car wireless sensor networks. These technologies span the radiofrequency (RF) range, from a few hundred MHz as in intra-car network of sensors to hundreds of GHz as in automotive radars used for in-vehicle occupant detection and advanced driver assistance systems. Vehicle occupants and road users in the vicinity of the connected vehicle are thus daily immersed in a multi-source and multi-band electromagnetic field (EMF) generated by such technologies. This paper is the first comprehensive and specific survey about EMF exposure generated by the whole ensemble of connectivity technologies in cars. For each technology we describe the main characteristics, relevant standards, the application domain, and the typical deployment in modern cars. We then extensively describe the EMF exposure scenarios resulting from such technologies by resuming and comparing the outcomes from past studies on the exposure in the car. Results from past studies suggested that in no case EMF exposure was above the safe limits for the general population. Finally, open challenges for a more realistic characterization of the EMF exposure scenario in the connected car are discussed.

Predicting human intention in vehicles, pedestrians and bicyclists interactions can help autonomous vehicles and human drivers to plan their routes in a safer manner and better optimise the use of road space. Several studies have studied human intention when interacting with other agents at crossroads using handcrafted features, motif analyses, and machine learning approaches. Yet, many of them are limited in accuracy due to relatively insufficient consideration of surrounding agents and limited observations (occlusions and inaccurate estimation of pose and location) confined by camera angles. This study utilised a multi-branch Gated Recurrent Unit encoder-decoder (MBGED) model to predict the intention of pedestrians and bicyclists when contenting with vehicles at intersections by analysing the properties of directly and indirectly involved road agents. This study identified decisive factors of human intention and constructed an encoder-decoder architecture based on those factors. The network was trained, validated, and tested on unsignalised and uncontrolled intersections. The system predicted the intention of vulnerable road users with 96% accuracy, 91% precision, and 93% recall at 2 seconds before the intersections happen, which could provide a reliable reference for autonomous vehicle navigation and advanced driver assistant systems.

Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS). When focusing on low-cost, large scale products for automated driving, model-driven approaches for the detection of lane markings have proven good performance. More recently, data-driven approaches have been proposed that target the drivable area / freespace mainly in inner-city applications. Focus of these approaches is less on lane-based driving due to the fact that the lane concept does not fully apply in unstructured, residential inner-city environments. So-far the concept of drivable area is seldom used for highway and inter-urban applications due to the specific requirements of these scenarios that require clear lane associations of all traffic participants. We believe that lane-based, mapless driving in inter-urban and highway scenarios is still not fully handled with sufficient robustness and availability. Especially for challenging weather situations such as heavy rain, fog, low-standing sun, darkness or reflections in puddles, the mapless detection of lane markings decreases significantly or completely fails. We see potential in applying specifically designed data-driven freespace approaches in more lane-based driving applications for highways and inter-urban use. Therefore, we propose to classify specifically a drivable corridor of the ego lane on pixel level with a deep learning approach. Our approach is kept computationally efficient with only 0.66 million parameters allowing its application in large scale products. Thus, we were able to easily integrate into an online AD system of a test vehicle. We demonstrate the performance of our approach under challenging conditions qualitatively and quantitatively in comparison to a state-of-the-art model-driven approach. We see the current approach as part of a fallback path whenever model-driven approaches cannot cope with a challenging scenario. We give insights how such a fallback path can be integrated into an AD system, thereby extending the overall system availability.

Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.

Driving has become a collaborative activity and a form of human-autonomy teaming (HAT) with the addition of autonomy to the advanced driver assistance system (ADAS), which makes situational decisio...

The design and implementation of digital systems with external configuration circuit–SHL (software–hardware–logic)—with standard cells based on floating-gate transistor (NeuMOS) and the implementation of the floating-gate potential diagram (FPD) to obtain Boolean functions are presented. External circuit configuration for designing digital systems of two and four bits reduces the number of transistors, as well as interconnection, and this can increase the speed of data processing; these results are helpful in growing applications like advanced driver assistance system (ADAS), where the number of sensors and processing time are increasing. Simulations results shown prove an effective reduction in transistor count, and a good performance of the system is demonstrated, as well.

The force transmitted from the front tires and tie rods to the steering rack of a vehicle, called the rack force, significantly influences the torque experienced by a driver at the steering wheel. As a result, estimates of rack force are used in a wide variety of advanced driver assist systems. Existing methods for producing rack force estimates are either susceptible to steering system disturbances or are only applicable for driving on roads with low frequency profile variations such as road slopes. In this paper we present a model that can produce disturbance-free rack force estimates for driving on roads with high frequency profile variations, such as road cleats and potholes, in addition to roads with low frequency profile variations. We validate the estimation accuracy of our model by presenting results from two driving experiments that were performed on test tracks with known low and high frequency road profile variations. We further demonstrate the merits of our model relative to the existing models by comparing the various estimates to rack force measurements obtained using a sensor mounted in the test vehicle., Comment: Accepted to 2020 IFAC World Congress

For market introduction of advanced driver assistant (ADAS) and automated driving (AD) systems on full vehicle level, testing and validation is one of the biggest challenges. The present study describes a novel approach that integrates a driving simulator in a virtual development process aiming to reduce time and effort for system development. The approach is demonstrated on a specific automated lane change assist (LCA) system. To this end, the LCA function and the corresponding human machine interface (HMI) are developed and implemented in the driving simulator. The core of the approach is a driving simulator-based testing method which proposes a novel two stage testing concept and involves multiple test drivers. The method provides better insight into the overall system performance and, moreover, detects potentials for improvements dedicated for the ADAS functionalities as well as for the design of the HMI system. Using this method, a driving simulator study with 20 volunteer drivers is conducted to evaluate the LCA system with respect to driver acceptance and user friendliness. The results of the study will be used for the parametrization and fine tuning of the LCA function as well as for the HMI improvement.

Most of the global automotive companies have been paid great efforts for reducing the accidents by developing an Advanced Driver Assistance System (ADAS) as well as autonomous vehicles. Lane detect...

Purpose: The purpose of this study is to verify factors affecting initial use intention of advanced driver assistance systems (ADAS) by adopting aspects of drivers’ safety and convenience. Design/m...

Editor’s note: Assurance of functional safety is a critical component of automotive SoC design, specifically in the context of autonomous driving and advanced driver assistance. This tutorial, written based on the authors’ strong industry experience, outlines the key challenges associated with functional safety and practices as well as standards to address them.—Swarup Bhunia, Case Western Reserve University

Various advanced driver assistance systems (ADAS) have been developed to improve drivers’ behavior and perceptual ability; however, whether these ADAS have any measurable effect on driving performance needs to be verified by field operational tests. The purpose of this study was to evaluate the effectiveness of ADAS on Chinese drivers as well as any possible influences of roadway type, gender and experience on driving performance, which can be measured by several variables, including longitudinal, lateral and braking behavior. The ADAS used in this study was a Mobileye M630 with forward collision warning (FCW) and lane departure warning (LDW) functions. Thirty-two participants were recruited to drive a vehicle equipped with Mobileye M630. Participants drove the same test route twice. The route consisted of a 12 km urban road, 34 km urban expressway and 45 km freeway as well as a 14 km adaption road. Vehicle dynamics, environmental information and driving operational data was recorded by CAN (Controller Area Network) bus and video cameras. The results show that ADAS significantly affects braking behavior. Braking time increased and relative speed decreased when drivers were exposed to ADAS. The ADAS also significantly affects several longitudinal behaviors, including the longitudinal deceleration and time headway (THW). The occurrence of critically low THW decreased in the experiment. However, there was no significant effect on lateral behavior. Furthermore, driver acceptance of the FCW function was much higher than the LDW function, and acceptance on the expressway and freeway was much higher than on the urban road. The results also reveal the significant influence of road type and experience on driving behaviors. These findings support policy development and technology improvements for future development of ADAS.

The rapid growth of the information technology industry in recent decades requires an increasing number of programmers and engineers, as well as an excellent training environment for relevant human resources. Currently, school curricula promote information education and, since 2012, “measurement and control by programs” have become compulsory topics of study in the technology and home economics (technological field) class in junior high schools. In addition, it has been determined that programming education will be made a compulsory topic in elementary schools from 2020 onward. Thus, programming education is included in the mandatory education program and, accordingly, various associated teaching materials have been developed and distributed to date. However, adequate steps have not been taken to improve the information education skill of the teachers who use the aforementioned teaching materials. In particular, only very few teachers understand the essence of programming control, such as the role played by a program in measurement and control. Hence, there is an urgent need to train teachers to make them capable of providing programming education in school settings. Hence, in this study, we develop a teaching material that helps teachers understand the flow of measurement and control by program using an optical distance sensor and an actuator and design a class practice for the students in the faculty of education. Finally, based on this education practice, we discuss an educational effect of the aforementioned teaching material and its applicability in junior high school settings.

The purpose of this research is to develop an advanced driver assistance system for the integrated longitudinal and lateral guidance of vehicles in critical high-speed lane change manoeuvres. The system consists of two parts: trajectory planning and combined control. At the first, by considering the TV position and the available range of longitudinal acceleration, several trajectories with different accelerations are generated. Then, by taking into account the vehicle and tyre dynamics, the most appropriate trajectory is selected. Therefore, the chosen trajectory is collision free and dynamically feasible. Because the trajectory planning is carried out algebraically, it has low computational cost. This is especially valuable in the experimental implementations. At the second part of the study, using a robust combined longitudinal-lateral controller, the control inputs are determined and transmitted to the brake/throttle and steering actuators. Both in the trajectory planning and combined control design, the nonlinear tyre dynamics and the dynamics of throttle and brake actuators are considered. To evaluate the performance of the proposed guidance algorithm, a full CarSim dynamic model is utilized. The simulation results for critical high-speed lane change manoeuvres confirm that the proposed trajectory planning method works effectively. The tracking error is also very small and the yaw stability is guaranteed.

In order to improve road safety, many advanced driver assist systems (ADAS) have been developed to support human-decision making and reduce driver workload. Currently, the majority of ADAS employ a single, often very simple, driver model to predict human-driver interaction in the immediate future (e.g., next few seconds). However, there is tremendous variability in how each individual drives, necessitating personalized driver models, based on data collected from observed actual driver actions. Yet, because we currently lack sufficient knowledge of the high-level cognitive brain functions, traditional control-theoretic driver models have difficulty accurately predicting driver actions. Recently, machine-learning algorithms have been utilized to predict future driver control actions. We compare several of these algorithms used to predict the lateral control actions of human drivers. Specifically, we compare these algorithms in terms of their suitability to develop haptic-shared ADAS, which share the control force with the human driver. To this end, we need to know how the steering torque is provided by the driver. However, low-cost driving simulators typically measure steering angle but not steering torque. Thus, this work also proposes a methodology to estimate the steering-wheel torque. Using the estimated steering torque, we train several machine learning driver control models and compare the performance using both simulated and real human-driving data sets.

ADAS (Advanced Driver Assistance System) are systems that control vehicles using environmental sensors such as radar and laser, helping the driver to recognize and react to potentially dangerous traffic conditions, mainly designed to increase driving comfort and traffic safety. In this study, the subcomponents of ADAS application on Zynq-7000 SoC (system on chip) has been introduced. Using computer vision, image processing and artificial intelligence frameworks, an ADAS application on Zynq-7000 SoC (system on chip), will detect road lanes from the camera on the vehicle in real-time and simultaneously notify the driver about them with high FPS. After applying preprocessing, a convolutional neural network is performed on the image to detect the road lines.

Advanced Driver Assistance Systems (ADAS) have become increasingly common in vehicles in the last decade. The majority of studies has focused on smaller vehicles with gross vehicle weight rating (GVWR) under 5,000lbs, predominantly sedans, for their ADAS evaluations. While it is sensible to use this style of vehicle because it is ubiquitous worldwide for a typical vehicle body style, these studies neglect full-size light-duty pickup trucks (FSLDPTs), GVWR 5,000 – 10,000lbs, which are abundant on the roads in the United States, 18% of vehicles. The increase in mass, higher center of gravity, and utilitarianism of the vehicles allows for unique conditions for studying the effects of ADAS. This work determines the best and worst location to be hit in a full-size light-duty pickup truck based on data for the industry sales leader in this class of vehicles. The objective is to use these results for future designs of ADAS technologies and their placement on the FSLDPT. While these methods could be applied to any vehicle, the FSLDPT sales leader will be investigated as it represents about 9% of registered vehicles in the United States. The results will be optimized with respect to cost in terms of initial up-front purchasing cost and post-accident vehicle repair cost.

Lane detection plays an important role in both advanced driver assistance system and autonomous vehicle domains. Benefited from the recent progress of deep learning methods, lane detection becomes more and more powerful. However, these methods usually require a high amount of numerical computation, and the performance of the algorithm always suffer from low speed due to the essential constraint of computing resources. In this paper, we propose a light-weight model which can simultaneously detect lanes in complex environments. Furthermore, a hierarchical feature fusion mechanism is proposed to refine detection module by producing high-level feature representations that are amenable to capture both rich object context and high-resolution details. Experiment results show that our proposed method achieves comparable performance with the state-of-the-art methods, with significantly improved computational efficiency.