Driver Assistance Systems Research Articles

State of the Art: Ongoing Research in Assessment Methods for Lane Keeping Assistance Systems

A safe, comfortable and intelligent Lane Keeping Assistance System (LKAS) is fundamental on the way to realizing full automated driving. In spite of the current high market penetration of LKAS, vehicle manufacturers, suppliers and researchers are still working on improving the system to reach a higher customer acceptance. To achieve this goal more efficiently, a systematic and ideally objective assessment procedure of the system needs to be established. Till date, a thorough review study of the assessment procedures of LKAS does not exist, especially of those which also take the subjective impression of the driver into consideration. The motivation of this paper is to benchmark the state-of-the-art assessment methods of LKAS and identify where problems and research potentials lie. The paper summarizes the relevant characteristics of LKAS based on representative research in the past decades. It also compares the characterization of LKAS with the field of steering feel and vehicle handling in the aspects of procedure, strategy, as well as their advantages and disadvantages. This paper contributes to transferring the know-hows and valuable experiences from other well-developed fields of vehicle dynamics into the rapid-evolving branch of Advanced Driver Assistance System (ADAS), in order to help standardizing its assessment procedure and give insights on system design from a human-centered perspective in the early development phase, thus shortening the ADAS development cycles.

Cognitive Aspect of Emotion Estimation of a Driver

Despite rapid advancements in the automotive industry, traffic safety risks persist. Addressing this challenge requires innovative driver assistance technologies. Common accidents result from driver inattention, fatigue, and stress, leading to issues like falling asleep at the wheel and improper acceleration and braking. Our study aims to contribute to advanced driver assistance systems that adapt to drivers' emotional needs, ultimately enhancing road safety. In this paper, we mention the result of our research to estimate drivers' emotions using sensors. For that purpose, we developed a sensor network containing sensors such as EEG, eye tracker, and driving simulator. We explored the relationship. As a result, we confirmed the relation between the driver's emotions, especially sleep conditions, driving speed, duration, and brain wave behavior.

Learning Human Behavior in Shared Control: Adaptive Inverse Differential Game Approach.

To enhance the collaborative intelligence of a machine, it is important for the machine to understand what behavior a human may adopt to interact with the machine when performing a task in shared control. In this study, an online behavior learning method is proposed for continuous-time linear human-in-the-loop shared control systems by using the system state data only. A two-player nonzero-sum linear quadratic dynamic game paradigm is used for modeling the control interaction between a human operator and an automation that actively compensates for human control action. In this game model, the cost function representing the human behavior is assumed to have an unknown weighting matrix. Here, we want to learn the human behavior or retrieve the weighting matrix by using the system state data only. Accordingly, a new adaptive inverse differential game (IDG) method, which integrates concurrent learning (CL) and linear matrix inequality (LMI) optimization, is proposed. First, a CL-based adaptive law and an interactive controller of the automation are developed to estimate the feedback gain matrix of the human online, and second, an LMI optimization problem is solved to determine the weighting matrix of the human cost function. Finally, simulation results on a cooperative shared control driver assistance system are provided to elucidate the feasibility of the developed method.

Secure Over-the-Air (OTA) Update Mechanisms for ADAS

This research aims to develop robust, resilient, and user-friendly over-the-air (OTA) software update mechanisms for advanced driver assistance systems (ADAS) in modern vehicles. The study conducts a comprehensive review on existing OTA update approaches, identifies security threats and vulnerabilities, and proposes an algorithm that incorporates cryptographic techniques, secure authentication, firmware validation, and rollback protection. The methodology utilized in this study involves secure package generation, distribution, installation, and monitoring, as well as adhering to automotive cybersecurity standards, such as ISO/SAE 21434 and UNECE WP.29. Extensive evaluation of OTA systems is important as it helps to assess their resilience against adversarial attacks, ensures regulatory compliance, and optimizes usability through user studies. The results demonstrate successful execution of secure OTA update mechanisms, preventing risks, fostering reliability and safety of ADAS software updates. The case studies highlight real-world deployments, best practices, and the effectiveness of the developed solution in improving automotive cybersecurity and functional safety.

Advancements in Mixed Reality for Autonomous Vehicle Testing and Advanced Driver Assistance Systems: A Survey

Advancements in Mixed Reality for Autonomous Vehicle Testing and Advanced Driver Assistance Systems: A Survey

A Virtual Vehicle–Based Car‐Following Model to Reproduce Hazmat Truck Drivers’ Differential Behaviors

Enhancing hazmat truck safety through advanced driving assistance systems (ADAS) relies on both system efficacy and driver reactions. This study investigates the driving behaviors of hazmat truck drivers in response to forward collision warnings (FCWs). Traditional warning triggering methods struggle to capture diverse and immediate driver responses; therefore, our research employs a vision‐based framework for driving data extraction and utilizes the K‐means++ clustering method for response‐based classification. Moreover, we propose an enhanced version of the intelligent driver model (IDM) based on the concept of a virtual vehicle to reproduce hazmat truck drivers’ differential behaviors during risky car‐following periods, achieving results that depict improved driving simulations. This model is compared with classic benchmarks, including the IDM, optimal velocity model (OVM), and full velocity difference (FVD) model, demonstrating superior performance in terms of traffic stability and safety in extreme scenarios. Our findings highlight that preaction drivers tend to accelerate before receiving warnings, opting to overtake rather than maintain safe distances. In contrast, calm drivers decelerate in anticipation of the warning, showcasing their awareness of maintaining safety. The analysis reveals that aggressive drivers are predominantly in the 41–45 age group, indicating a higher skill level, while calm drivers are more commonly older, reflecting a trend in cautious driving behaviors. Overall, our research contributes to the development of effective ADAS by considering real‐time driver responses and emphasizes the potential of our model to revolutionize commercial ADAS adoption and enhance road safety for hazmat operations.

Performance and Environment-aware Advanced Driving Assistance Systems

Performance and Environment-aware Advanced Driving Assistance Systems

Object Detection, Recognition, and Tracking Algorithms for ADASs-A Study on Recent Trends.

Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

The Application and Challenges of Artificial Intelligence in the Transportation Field

In this paper, we analyze the key role played by artificial intelligence (AI) technology in shaping the future of the automotive industry, aiming to provide a comprehensive guide for automotive enterprises embracing the transformative potential of AI integration. We begin by establishing the impact of AI in the transportation and automotive sectors, leading to an innovative AI solution: “Automotive Intelligent AI Assistant”. This solution harmonizes the functionalities of Advanced Driver Assistance Systems (ADAS) and Driver State Monitoring (DMS). The system aims to address inherent flaws in both systems while enhancing overall vehicle safety and performance. Through case analyses and challenge assessments, we show how enterprises can be empowered to gain a deep understanding of the implementation of this cutting-edge solution. Furthermore, we emphasize market potential, identify key concerns, and highlight the advantages offered by this integration.

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.

UTILIZING ADVANCED DRIVER ASSISTANCE SYSTEMS IN LATER LIFE: INSIGHTS FROM A LONGITUDINAL STUDY ON AGING DRIVERS

Abstract Level 1 and 2 Advanced Driver Assistance System (ADAS) technologies (e.g., adaptive cruise control, blind spot warning, lane keep assist) are predicted to be available in approximately three-fourths of all vehicles worldwide by 2025. To realize the potential of these systems in extending safe mobility for aging adults, it is critical that the driver understands the functionality of the technology and uses the system appropriately. The current study examined demographic and vehicle technology questionnaire data from the Longitudinal Research on Aging Drivers (LongROAD) cohort study which concluded in December 2022. A total of 1,417 participants changed their vehicle throughout the study. There were statistically significant increases in the prevalence of all 15 ADAS technologies examined. Despite increases in prevalence, frequency of using the technologies remained unchanged across 5 years. Frequency of use also varied by functionality of the technology whereby participants reported higher frequency of using technologies that provide alerts, such as blind spot warning, than technologies that take action to assist drivers with vehicle operations, such as adaptive cruise control. Results showed differences in prevalence and use of technologies by income and education, suggesting disparities in access to vehicles with technologies that could help to create a safer driving experience. In consideration of the rapid proliferation of ADAS into the vehicle fleet, increased research into how older drivers learn about and use ADAS technologies will assist in efforts to develop tailored and accessible programs for training older adults to properly utilize ADAS available in their own vehicles.

ADDRESSING THE COMPLEX DRIVING NEEDS OF AN AGING POPULATION: THE RISE OF AUTONOMOUS VEHICLES

Abstract Eighty-eight million Americans will be age 65 or older by 2050 and 25% of all drivers will be ≥ 65 years old. Technology has the potential to address some issues such as the inability to drive in advanced age. We postulate that continuing to drive with the use of Advanced Driver Assistance Systems (ADAS) and/or maintaining social connectedness with the use of autonomous vehicles (AVs) have the potential to slow down or reduce the impact of age-related diseases and enhance social mobility. ADAS are electronic technologies that assist drivers in safely operating a vehicle by detecting crash risk stimuli and reducing driver error. Older drivers’ willingness to use ADAS depends on individual perceived usefulness, safety, ease-of-learning, and anxiety. Based on the SAE levels of automation (0-5) , level 3-5 AVs can detect, evaluate, and respond to the vehicle environment without human involvement. Still, ADAS and AV technology cannot completely supplant all human driving and it cannot independently determine crash risk and safety. Comprehensive studies are needed to prove the effectiveness of ADAS in reducing crash risk among older drivers, especially those who are most vulnerable. In addition, AV manufacturers need to take into account the requirements and needs of providing this type of mobility service to older adults, especially those with chronic neurodegenerative diseases like dementia. If these technologies are eventually shown to be safe and embraced by older adults, they have the potential to delay driving cessation, prolong quality of life and maintain social connectedness.

Exploring perceptions of Advanced Driver Assistance Systems (ADAS) in older drivers with age-related declines

Perceptions of Advanced Driver Assistance Systems (ADAS) were explored in two semi-structured face-to-face focus group studies of 42 older drivers (aged 65 years and older) with and without age-related declines. Study 1 explored perceptions regarding ADAS, focusing on visual, auditory, physical, and cognitive factors. Study 2 extended this by additionally exploring perceptions following exposure to videos and stationary vehicle demonstrations of an ADAS. Participants had a range of visual, hearing, memory, and health characteristics which impacted on their daily life. In both studies, some participants had insights regarding various ADAS technologies prior to the study, but many were unfamiliar with these systems. Nevertheless, overall, participants reported that ADAS would assist them to drive as they age and increase their mobility and independence. There were comments regarding the benefits of warning alerts, although the potential for them to be distracting was also highlighted. Participants with vision impairment preferred audio alerts and participants with hearing impairment preferred visual display alerts. Findings highlighted the potential for ADAS to assist those with age-related declines and the need to increase the flexibility of warning system alerts to suit the varying requirements of older drivers, as well as to reduce the complexity of vehicle interfaces. Collectively, these strategies would maximize the benefits of these vehicles to increase the mobility, independence, and quality of life of older drivers with and without age-related declines.

GENDER AND AGE DIFFERENCES IN ACCEPTANCE OF ADVANCED DRIVER ASSISTANCE SYSTEMS: INSIGHTS FROM OLDER AUSTRALIANS

Abstract Advanced Driver-Assistance Systems (ADAS) are in-vehicle technologies that promise to improve driver safety and may help support older drivers to drive safer for longer, however there is little research examining acceptance and use of ADAS among older adults. This study investigated age and gender differences in attitudes to ADAS and use of ADAS. We conducted an online survey of 1330 drivers aged 65 years or older (M=72, SD=6.9, 23% women) in partnership with National Seniors Australia. ADAS use was self-reported, classifying respondents in to users (96%) and non-users (4%). ADAS acceptance was measured by the Partial Automation Acceptance Scale. Linear regression estimated gender and age differences in acceptance. Logistic regression estimated differences in ADAS use by gender, age and acceptance. Older age was associated with higher levels of trust in ADAS (β = 0.032, p = 0.04) and lower perceived ease of use (β = -0.02, p = 0.02). Women were less likely to report ADAS use (β = -0.99, p = 0.008) but had higher levels of trust (β = 0.86, p = 0.001) than men. After adjusting for age and gender, positive attitudes towards ADAS (β = 0.12, p = 0.003) and lower perceived risk (β = 0.22, p = 0.046) were associated with higher levels of ADAS use. Consideration of the gender and age differences may inform future vehicle design, and older drivers may benefit from education on the risks and benefits of ADAS to aid in the acceptance and consequent use of ADAS.

What Road Elements are More Important than Others for Safe Driving on Urban Roads?

Road elements are increasingly digitalized to provide drivers advanced assistance especially in the emergent or adverse conditions. It is challenging and expensive to accurately digitalize all the road elements especially on the urban roads with many infrastructures and complex designs, where we may focus on the most important ones at the first stage. This research designs a questionnaire to ask the drivers to rank the importance of the road elements in various driving conditions. Driver characteristics are also collected, including age, driving style, accident experience, and accumulated driving distance, to explore their effect on drivers’ cognition of road elements importance. It is found that driving is a complex activity, and the moving elements (e.g. surrounding cars) are more important than the non-moving ones. Attention should be paid to the road elements even distant from the ego car, to get prepared to the potential driving risk or penalty. Statistical difference between the experienced and non-experienced drivers recommends that driver assistance system should be sufficiently trained in various conditions, to build up autonomous driving tactics and skills. This research promotes the understanding of driving cognition pattern to provide insights into the development of road digitalization.

TIR‐YOLO‐ADAS: A thermal infrared object detection framework for advanced driver assistance systems

AbstractAn object detection framework using thermal infrared (TIR) cameras is proposed to meet the needs of an advanced driver assistance system (ADAS) operating at night‐time and in low‐visibility conditions. The proposed detection framework, referred to as TIR‐YOLO‐ADAS, is an improvement of YOLOX for TIR object detection in ADAS. First, to address the disadvantages of TIR objects, the part of the attention mechanism is designed to enhance the discriminative ability of feature maps in the spatial and channel dimensions. Second, a focal loss function is used as the confidence loss function to enable the framework to focus on detection tasks of difficult, misclassified targets in the process of network training. The results of the ablation experiment on the Forward‐looking infrared (FLIR) thermal ADAS dataset indicate that the proposed framework significantly improves the performance of TIR object detection. Comparative experimental results further show that TIR‐YOLO‐ADAS performs favourably when compared with three representative detection algorithms. To evaluate the practicality and feasibility of the proposed framework in various applications, a qualitative assessment in real road scenarios was conducted. The experimental results confirm that the proposed framework performs promisingly and could be integrated into vehicle platforms as an ADAS module.

Accelerated Augmented Reality Holographic 4k Video Projections Based on Lidar Point Clouds for Automotive Head‐Up Displays

AbstractIdentifying road obstacles hidden from the driver's field of view can ensure road safety in transportation. Current driver assistance systems such as 2D head‐up displays are limited to the projection area on the windshield of the car. An augmented reality holographic point cloud video projection system is developed to display objects aligned with real‐life objects in size and distance within the driver's field of view. Light Detection and Ranging (LiDAR) point cloud data collected with a 3D laser scanner is transformed into layered 3D replay field objects consisting of 400 k points. GPU‐accelerated computing generated real‐time holograms 16.6 times faster than the CPU processing time. The holographic projections are obtained with a Spatial Light Modulator (SLM) (3840×2160 px) and virtual Fresnel lenses, which enlarged the driver's eye box to 25 mm × 36 mm. Real‐time scanned road obstacles from different perspectives provide the driver a full view of risk factors such as generated depth in 3D mode and the ability to project any scanned object from different angles in 360°. The 3D holographic projection technology allows for maintaining the driver's focus on the road instead of the windshield and enables assistance by projecting road obstacles hidden from the driver's field of view.

Traffic Sign Detection in Complex Scenarios based on YOLOV7

In recent years, thanks to the development of the automobile industry and the advancement of industrial intelligence, driving assistance systems have been developing rapidly under the requirements of national policies and the demands of the consumer market. Traffic sign detection module, as an indispensable part of the driving assistance field, is in urgent need of new target detection algorithms in this field to solve the problems of poor detection accuracy, low computing speed and poor adaptability of the existing algorithms to the vehicle hardware. To solve the application problem, in this paper, we propose a traffic target detection algorithm based on YOLOV7. The model is trained using the CCTSDB dataset and tested in complex scenarios such as different lighting conditions and weather conditions. The mAP of YOLOV7 is stable at over 82.46% and the mAP value of YOLOV7-tiny is stable at over 78.57%, demonstrating the effectiveness of our method that it has the ability to detect and recognize our traffic signs in practical scenarios. In addition, this paper conducts a comparative study on the commonalities and differences of the above two models and draws the corresponding conclusions. In summary, YOLOV7 series can be effectively used in traffic sign detection work.

Editorial

In the rapidly evolving landscape of mobility technology, the ARAI Journal of Mobility Technology takes pride in presenting its Oct–Dec 2023 issue—a testament to the relentless pursuit of innovation and excellence within the automotive realm.
 The world is at a pivotal juncture, demanding sustainable solutions to mitigate the environmental impact of transportation. This journal delves into ground-breaking advancements in electric and hybrid vehicles, shedding light on technological strides that promise to reshape the future of mobility. As we stand on the brink of a cleaner, greener era, the ARAI Journal is your guide to understanding and embracing sustainable mobility.
 Autonomous vehicles have transitioned from fiction to reality, and this journal explores the latest breakthroughs in autonomous driving technology. From cutting-edge sensor systems to artificial intelligence algorithms, discover how researchers and engineers are propelling us toward a future where vehicles navigate with unparalleled precision, safety, and efficiency.
 The digital revolution has transformed the automotive industry. With the advent of 5G and beyond, this journal explores the transformative impact of connectivity on vehicles. From smart infrastructure to advanced driver-assistance systems, discover how the fusion of technology and automotive engineering is creating a connected ecosystem that enhances the driving experience and ensures unprecedented levels of safety.
 Innovation is not without its challenges, and this journal candidly addresses the hurdles faced by the mobility technology sector. From regulatory considerations to cybersecurity concerns, our editorial team examines the complexities that accompany progress, providing insightful perspectives on navigating the intricate landscape of modern automotive development.
 The issue shines a spotlight on ground-breaking research and development initiatives. Whether it is materials engineering for lightweight or the exploration of alternative fuel sources, the ARAI Journal is your gateway to the latest discoveries and advancements driving the automotive industry forward.
 As we embark on a new era of mobility, this issue of the ARAI Journal of Mobility Technology serves as a compass, guiding industry professionals, researchers, and enthusiasts through the dynamic landscape of technological innovation. Together, let us embrace the challenges, celebrate the successes, and collectively steer the course toward a future where mobility is not just a means of transportation but a catalyst for positive global change.

DATASET AND IMPROVED YOLOV7 FOR TEXT-BASED TRAFFIC SIGN DETECTION

Abstract. Traffic sign detection is an important part of autonomous driving technology, and it is also important to have a large-scale dataset applicable to Chinese traffic scenarios. The article proposes a text-based self-labelled traffic sign dataset which consists of 3153 images, of which 2903 images are used for training and 250 images are used for validation. And an improved YOLOv7 algorithm is provided that incorporates the BiFormer attention mechanism into the YOLOv7 network to enhance its ability to detect small objects. This approach has the advantage of improved accuracy but may increase runtime. To mitigate this problem, the improved YOLOv7 network undergoes model pruning to compress the model size and increase its speed. Experimental results show that the improved YOLOv7 network in this paper improves the average accuracy by 2.9% while maintaining almost the same speed as the original network. After testing, the model has a real-time effect and practical significance. In conclusion, the text-based self-annotated dataset and the improved YOLOv7 network proposed in this paper have important reference values for text-based traffic sign recognition in automatic driving assistance systems.