Driver Assistance Systems Research Articles

ADAS and automation-arelevant contribution to maintaining mobility of older drivers?

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have ahigh level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be apriority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in acritical situation.

Real Time Traffic Sign Recognition Using Integrated Camera Sensors and YOLOV8 Algorithm

Traffic signs are an important element in maintaining smoothness and safety on the road. However, there are still many drivers who violate them, causing various negative impacts. Traffic Sign Recognition (TSR) is a technology that has the ability to detect and identify various types of traffic signs by utilizing artificial intelligence in the computer vision domain. TSR has been applied in various vehicle applications, such as Advance Driver Assistance System (ADAS) and Autonomous Driving System (ADS). This system is made by integrating camera sensors with the YOLOv8 algorithm which has high accuracy and fast data processing. The data used were 2093 images and annotated through Roboflow. Then the data is trained through Google Collaboratory with a mAP evaluation result of 95.5% which shows that the system can detect objects accurately. The level of precision and success of the model in detecting objects is 93.5% and the success is 93.3%. The results of system testing can use images, videos, and through cameras in real time.

A robust and real-time lane detection method in low-light scenarios to advanced driver assistance systems

Lane detection, which relies on front-view RGB cameras, is a crucial aspect of Advanced Driver Assistance Systems (ADAS), but its effectiveness is notably reduced in low-light conditions. This issue is exacerbated by the lack of specialized datasets and generalizable methods for such scenarios. To address this gap, we introduce NightLane, a comprehensive dataset tailored for low-light, multi-traffic lane detection. We adhere to stringent data annotation guidelines, ensuring reliable detection accuracy. Additionally, we propose the Fused Low-Light Enhancement Framework (FLLENet), which leverages modern detection networks and incorporates a low-light enhancement module and attention mechanisms. The enhancement module, based on zero-reference learning, improves image quality and channel richness, while the attention mechanisms effectively extract and utilize these features. Our extensive testing on NightLane and CULane datasets demonstrates superior performance in low-light lane detection, showcasing FLLENet’s robust generalizability and efficacy. Specifically, our approach achieves an F1 measure of 76.90 on CULane and 78.91 on NightLane, demonstrating its effectiveness against state-of-the-art methods. We also evaluate the real-time applicability of our framework on a low-power embedded lane detection system using NVIDIA Jetson AGX/Orin, achieving high accuracy and real-time performance. Our work offers a new approach and reference in the field of low-light lane detection, potentially aiding in the ongoing enhancement of ADAS (ADAS). Dateset are available at https://github.com/pengjingt/FLLENet.

Deep learning approach for unified recognition of driver speed and lateral intentions using naturalistic driving data

Driver intention recognition is a critical component of advanced driver assistance systems, with significant implications for improving vehicle safety, intelligence, and fuel economy. However, previous research on driver intention recognition has not fully considered the influence of the driving environment on speed intentions and has not exploited the temporal dependency inherent in the lateral intentions to prevent erroneous changes in recognition. Furthermore, the coupling of speed and lateral intentions was overlooked; they were generally considered separately. To address these limitations, a unified recognition approach for speed and lateral intentions based on deep learning is presented in this study. First, extensive naturalistic driving data are collected, and information related to road slope and driving trajectories is extracted. A comprehensive classification of driver intentions is then performed. Toeplitz inverse covariance-based clustering and trajectory clustering methods are applied separately to label speed and lateral intentions, so that the influence of driving environments and the coupling of speed and lateral intentions are integrated into intention recognition. Finally, a deep-learning-based unified recognition model for driver intention is developed. This model uses a hierarchical recognition approach for speed intentions and includes a double-layer networks architecture with long short-term memory for the recognition of lateral intention. The validation results show that the created driver intention recognition model can accurately and stably recognize both speed and lateral intentions in complex driving environments.

Combining Optimization and Simulation for Next-Generation Off-Road Vehicle E/E Architectural Design.

The automotive industry, with particular reference to the off-road sector, is facing several challenges, including the integration of Advanced Driver Assistance Systems (ADASs), the introduction of autonomous driving capabilities, and system-specific requirements that are different from the traditional car market. Current vehicular electrical-electronic (E/E) architectures are unable to support the amount of data for new vehicle functionalities, requiring the transition to zonal architectures, new communication standards, and the adoption of Drive-by-Wire technologies. In this work, we propose an automated methodology for next-generation off-road vehicle E/E architectural design. Starting from the regulatory requirements, we use a MILP-based optimizer to find candidate solutions, a discrete event simulator to validate their feasibility, and an ascent-based gradient method to reformulate the constraints for the optimizer in order to converge to the final architectural solution. We evaluate the results in terms of latency, jitter, and network load, as well as provide a Pareto analysis that includes power consumption, cost, and system weight.

A Comparative Study of Recent Practices and Technologies in Advanced Driver Assistance System

A Comparative Study of Recent Practices and Technologies in Advanced Driver Assistance System

Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car.

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.

Lane Detection, Segmentation, Pothole Detection and Traffic Sign Recognition for ADAS

Lane detection, a critical aspect of advanced driver assistance systems (ADAS) and autonomous vehicles, is addressed in this work, combining classical computer vision methods with deep learning techniques. The proposed solution, utilizing a UNet-based model trained with TensorFlow and Keras, enhances vehicle perception for intelligent transportation systems. Integrated functionalities for pothole detection and traffic sign detection further contribute to safety and efficiency, enabling vehicles to identify road hazards and comply with regulations. The system encompasses data preprocessing, model training, and real-time video analysis, while a classical lane detection pipeline using OpenCV showcases various stages such as grayscale conversion, Gaussian blur, Canny edge detection, masking, Hough transform, and lane overlay. This comprehensive approach supports road safety, traffic management, and transportation efficiency initiatives, making significant strides in intelligent transportation systems and urban planning.

Research on Driver Style Recognition Based on GA-K-Means and PSO-SVM

<div>This article aims to address the challenge of recognizing driving styles, a task that has become increasingly complex due to the high dimensionality of driving data. To tackle this problem, a novel method for driver style clustering, which leverages the principal component analysis (PCA) for dimensionality reduction and an improved GA-K-means algorithm for clustering, is proposed. In order to distill low-dimensional features from the original dataset, PCA algorithm is employed for feature extraction and dimensionality reduction. Subsequently, an enhanced GA-K-means algorithm is utilized to cluster the extracted driving features. The incorporation of the genetic algorithm circumvents the issue of the model falling into local optima, thereby facilitating effective driver style recognition. The clustering results are evaluated using the silhouette coefficient, Calinski–Harabasz (CH) index, and GAP value, demonstrating that this method yields more stable classification results compared to traditional clustering methods. In the final stage, a particle swarm optimization-SVM (PSO-SVM) algorithm is applied to classify the clustering results, which are then compared with results from other machine learning algorithms such as decision tree, naive Bayes network, and K-nearest-neighbor (KNN). This comprehensive approach to driver style recognition holds promise for enhancing traffic safety and efficiency. The accurate recognition of driving style can lay the foundation for further optimization of advanced driver assistance systems (ADAS).</div>

Sensor-Fused Nighttime System for Enhanced Pedestrian Detection in ADAS and Autonomous Vehicles.

Ensuring a safe nighttime environmental perception system relies on the early detection of vulnerable road users with minimal delay and high precision. This paper presents a sensor-fused nighttime environmental perception system by integrating data from thermal and RGB cameras. A new alignment algorithm is proposed to fuse the data from the two camera sensors. The proposed alignment procedure is crucial for effective sensor fusion. To develop a robust Deep Neural Network (DNN) system, nighttime thermal and RGB images were collected under various scenarios, creating a labeled dataset of 32,000 image pairs. Three fusion techniques were explored using transfer learning, alongside two single-sensor models using only RGB or thermal data. Five DNN models were developed and evaluated, with experimental results showing superior performance of fused models over non-fusion counterparts. The late-fusion system was selected for its optimal balance of accuracy and response time. For real-time inferencing, the best model was further optimized, achieving 33 fps on the embedded edge computing device, an 83.33% improvement in inference speed over the system without optimization. These findings are valuable for advancing Advanced Driver Assistance Systems (ADASs) and autonomous vehicle technologies, enhancing pedestrian detection during nighttime to improve road safety and reduce accidents.

Custom Anchorless Object Detection Model for 3D Synthetic Traffic Sign Board Dataset with Depth Estimation and Text Character Extraction

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions.

Unveiling gap acceptance behaviour during lane change with EDIV data: A deep dive into driving behaviour on expressway using a three level mixed effect linear regression approach

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

Exploratory analysis of injury severity under different levels of driving automation (SAE Levels 2 and 4) using multi-source data

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles. To ensure a comprehensive analysis, a multi-source dataset that includes 1,001 ADAS crashes (SAE Level 2 vehicles) and 548 ADS crashes (SAE Level 4 vehicles) is used. Two random parameters multinomial logit models with heterogeneity in the means of random parameters are considered to gain a better understanding of the variables impacting the crash injury severity outcomes for the ADAS (SAE Level 2) and ADS (SAE Level 4) vehicles. It was found that while 67 percent of crashes involving the ADAS equipped vehicles in the dataset took place on a highway, 94 percent of crashes involving ADS took place in more urban settings. The model estimation results also reveal that the weather indicator, driver type indicator, differences in the system sophistication that are captured by both manufacture year and high/low mileage as well as rear and front contact indicators all play a role in the crash injury severity outcomes. The results offer an exploratory assessment of safety performance of the ADAS and ADS equipped vehicles using the real-world data and can be used by the manufacturers and other stakeholders to dictate the direction of their deployment and usage.

Driving Assistance System with Obstacle Avoidance for Electric Wheelchairs.

A system has been developed to convert manual wheelchairs into electric wheelchairs, providing assistance to users through the implemented algorithm, which ensures safe driving and obstacle avoidance. While manual wheelchairs are typically controlled indoors based on user preferences, they do not guarantee safe driving in areas outside the user's field of vision. The proposed model utilizes the dynamic window approach specifically designed for wheelchair use, allowing for obstacle avoidance. This method evaluates potential movements within a defined velocity space to calculate the optimal path, providing seamless and safe driving assistance in real time. This innovative approach enhances user assistance and safety by integrating state-of-the-art algorithms developed using the dynamic window approach alongside advanced sensor technology. With the assistance of LiDAR sensors, the system perceives the wheelchair's surroundings, generating real-time speed values within the algorithm framework to ensure secure driving. The model's ability to adapt to indoor environments and its robust performance in real-world scenarios underscore its potential for widespread application. This study has undergone various tests, conclusively proving that the system aids users in avoidance obstacles and ensures safe driving. These tests demonstrate significant improvements in maneuverability and user safety, highlighting a noteworthy advancement in assistive technology for individuals with limited mobility.

What is good? Exploring the applicability of a one item measure as a proxy for measuring acceptance in driver-vehicle interaction studies

New driver assistance systems play an important role to rise safety and comfort in todays´ traffic. Those systems should be developed with the needs of the user in mind and tested for the users´ requirements. In this, user acceptance is a central variable of interest – both in scientific and in practical applications of user-centered research on driver assistance systems. In some cases, applied research settings need simplified measurements in order to be efficiently applicable in the driving situations. In the present paper, we explored the applicability and validity of a single-item acceptance measurement (SIAM) for practical study settings covering the attitude towards using new driver assistance systems. To provide a theoretical framing, we tested the one-item measure against the widely used Technology Acceptance Model (TAM) and the van der Laan acceptance scale (VDL) in a driving simulator study. Participants experienced four different complex driving scenarios using a driver assistance system. Acceptance was measured repeatedly throughout the drive. The results supported construct validity for the SIAM, correlating with the VDL. The SIAM further predicted the intention to use the system. Being carefully aware of the psychometric drawbacks of short scales and acknowledging the importance of multi-item scales, the SIAM is promising for efficiently approaching the acceptance of driver assistance systems in applied settings.

Training benefits driver behaviour while using automation with an attention monitoring system

Attention, or more generally, driver monitoring systems have been identified as a necessity to address overreliance on driving automation. However, research suggests that monitoring systems may not be sufficient to support safe use of advanced driver assistance systems (ADAS), also evidenced by a recent major recall of Tesla’s monitoring software. The objective of the current study was to investigate whether different training approaches improve driver behaviour while using ADAS with an attention monitoring system. A driving simulator study was conducted with three between-subject groups: no training, limitation-focused training (highlighted situations where ADAS would not work), and responsibility-focused training (highlighted the driver’s role/responsibility while using ADAS). All participants (N = 47) experienced eight events which required the ego-vehicle to slow down to avoid a collision. Anticipatory cues in the environment indicated the potential for the upcoming events. Event type (covered in training vs. not covered) and event criticality (action-necessary vs. action-not-necessary) were within-subject factors. The responsibility-focused group made fewer long glances (≥ 3 s) to a secondary task than the no training and limitation-focused groups when there were no anticipatory cues. Responsibility-focused training and no training were associated with faster takeover time at the events than limitation-focused training. There were additional benefits of responsibility-focused training for events that were covered in training (e.g., higher percent of time looking at the anticipatory cues). Overall, our results suggest that even if attention monitoring systems are implemented, there may be benefits to driver ADAS training. Responsibility-focused training may be preferable to limitation-focused training, especially for situations where minimizing training length is advantageous.

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies.

Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method's adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.

Radar-Based Pedestrian and Vehicle Detection and Identification for Driving Assistance

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology.

Analysis of driving left-turn errors of young and older drivers at urban intersections

Numerous traffic crashes occur at intersections in urban areas. Understanding the mechanism of driving errors at intersections and providing effective solutions will significantly reduce traffic injuries. The objective of this study is to examine the age and hazard level’s effects on driver left-turn errors at urban intersections. In order to achieve this goal, this study customized the cognitive reliability and error analysis method (CREAM) for left-turn driving task at intersections, and driver error profiles were investigated using a driving simulation experiment as a case study. The results showed that age did not significantly affect drivers’ left turn error probability, while the error probability of both groups of drivers increased with the hazard level. Drivers were most likely to have inadequate monitoring and misjudgment in left-turn maneuvers, more so in the older group. Young drivers were more likely to make planning, decision-making, and adjustment errors compared to older drivers. In addition, observation errors were the most likely cognitive (74.5%). This study achieves the common and characteristic performance of human errors under the three hazardous intersections for the two age groups, and provides a basis for the design of effective training modules and driver assistance systems.

Ultra-Fast Nonlinear Model Predictive Control for Motion Control of Autonomous Light Motor Vehicles

Advanced Driver Assistance System (ADAS) is the latest buzzword in the automotive industry aimed at reducing human errors and enhancing safety. In ADAS systems, the choice of control strategy is not straightforward due to the highly complex nonlinear dynamics, control objectives, and safety critical constraints. Nonlinear Model Predictive Control (NMPC) has evolved as a favorite option for optimal control due to its ability to handle such constrained, Multi-Input Multi-Output (MIMO) systems efficiently. However, NMPC suffers from a bottleneck of high computational complexity, making it unsuitable for fast real-time applications. This paper presents a generic framework using Successive Online Linearization-based NMPC (SOL-NMPC) for for the control in ADAS. The nonlinear system is linearized and solved using Linear Model Predictive Control every iteration. Furthermore, offset-free MPC is developed with the Extended Kalman Filter for reducing model mismatch. The developed SOL-NMPC is validated using the 14-Degrees-of-Freedom (DoF) model of a D-class light motor vehicle. The performance is simulated in matlab/Simulink and validated using the CarSim® software (Version 2016). The real-time implementation of the proposed strategy is tested in the Hardware-In-the-Loop (HIL) co-simulation using the STM32-Nucleo-144 development board. The detailed performance analysis is presented along with time profiling. It can be seen that the loss of accuracy can be counteracted by the fast response of the proposed framework.