Driver Assistance Systems Research Articles

Real-Time Driver Drowsiness Detection System Using Machine Learning

Driver fatigue is a leading cause of road accidents globally, with significant fatalities and injuries reported annually. This paper presents a real-time Driver Drowsiness Detection System leveraging computer vision and machine learning techniques. Using Convolutional Neural Networks (CNNs), the system monitors key behavioural indicators, such as eye closure and yawning frequency, from live video feeds. Preprocessing techniques like grayscale conversion and normalization ensure robustness under varied conditions. Alerts are triggered immediately upon detecting drowsiness, enhancing road safety. The system’s non-intrusive design ensures scalability and affordability, making it suitable for integration into Advanced Driver Assistance Systems (ADAS). This paper also discusses challenges and potential enhancements, such as IoT integration and edge computing. Key Words: Driver Drowsiness Detection, Machine Learning, CNNs, Real-Time Monitoring, Road Safety.

Mounting Angle Prediction for Automotive Radar Using Complex-Valued Convolutional Neural Network.

In advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance. This paper introduces the Automotive Radar Alignment Detection Network (AutoRAD-Net), a novel model that leverages complex-valued convolutional neural network (CV-CNN) to address azimuth misalignment challenges in automotive radars. By utilizing complex-valued inputs, AutoRAD-Net effectively learns the physical properties of the radar data, enabling precise azimuth alignment. The model was trained and validated using mounting angle offsets ranging from -3° to +3° and exhibited errors no greater than 0.15° across all tested offsets. Moreover, it demonstrated reliable predictions even for unseen offsets, such as -1.7°, showcasing its generalization capability. The predicted offsets can then be used for physical radar alignment or integrated into compensation algorithms to enhance data interpretation accuracy in ADAS applications. This paper presents AutoRAD-Net as a practical solution for azimuth alignment, advancing radar reliability and performance in autonomous driving systems.

Rethinking Advanced Driver Assistance System taxonomies: A framework and inventory of real-world safety performance

Rethinking Advanced Driver Assistance System taxonomies: A framework and inventory of real-world safety performance

Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review

Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review

Advanced Pedestrian Distance Estimation for ADAS with Canny Edge Detection and Stereo Vision

Pedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision cameras are well-regarded for their effectiveness and affordability in measuring depth through disparity between two images. Despite this, research on pedestrian distance estimation using only stereovision remains sparse, with many studies relying on computationally heavy dense depth maps. This paper proposes an innovative method for computing object-level disparity specifically for pedestrian detection using stereo cameras. The approach integrates Canny edge detection with ORB (Oriented FAST and Rotated BRIEF) feature matching to efficiently identify and track keypoints within pedestrian bounding boxes. This method not only improves the accuracy of distance estimation but also reduces computational demands, making it suitable for real-time applications. The approach was thoroughly tested on a Raspberry Pi 4, a resource-constrained device, and achieved promising results, demonstrating its potential for practical use in ADAS.

Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data

Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data

Performance and Environment-Aware Advanced Driving Assistance Systems

Performance and Environment-Aware Advanced Driving Assistance Systems

PREDICTING USE OF ADVANCED DRIVER-ASSISTANCE SYSTEMS IN OLDER DRIVERS THROUGH STRUCTURAL EQUATION MODELING

Abstract Older drivers are an at-risk population when on-road. Normal ageing processes, such as declines in visual, sensorimotor and cognitive processes, impact driving skills and contribute to the increasing crash rate. Advanced Driver-Assistance Systems (ADAS) may provide a solution to keep older drivers safe on the road. This study investigated what technology acceptance and trust factors predict use of ADAS between older drivers who have ADAS in their car and a) use it (n=790) or b) do not use it (n=32). We conducted an online survey to 1330 Australian drivers aged 65 years or older (M=72, SD=6.9, 23% women) in partnership with National Seniors Australia. ADAS acceptance was measured by the Partial Automation Acceptance Scale, measured by the latent factors attitudes towards ADAS, attitudes towards technology, perceived risk and trust. Structural equation modeling examined the structural relationships between use of ADAS, the acceptance scale, locus of control and driver self-efficacy. The results indicated positive attitudes towards ADAS had a significant positive direct effect on use (y =.06, S.E =.02, p =.002 (β =.26)) and trust in ADAS had a significant negative direct effect on use (y = -.03, S.E =.01, p =.024 (β = -.15)). The model tested accounted for 93.4% of the variance in use of ADAS. These findings highlight the importance of older drivers to hold positive attitudes and trust in ADAS in order for them to use it, highlighting potential factors that could be targeted in behavioural change interventions seeing to improve usage rates.

Driving Safer: A Look at How Men and Women Use Advanced Driver Assistance Systems

Objective — This study investigates the nuanced landscape of gender-specific patterns in utilization of Advanced Driver Assistance Systems (ADAS), aiming to contribute insights to enhance road safety. Methods — Leveraging data from diverse sources, including Scopus, Web of Science, and Google Scholar, the method employed involves a meticulous examination of studies exploring gender differences in driving behaviors and ADAS adoption. Key factors influencing these differences, were analyzed including age, experience, and attitudes toward technology. Cultural and societal influences on gender-specific driving behaviors are explored, shedding light on the intricate interplay of perceptions and expectations. Results — The review identifies challenges in ADAS adoption, including limited awareness, misconceptions, and resistance to change. Opportunities for improvement are then outlined, encompassing comprehensive user training, clear communication of ADAS features, customizable settings, and incentives for ADAS-equipped vehicles. This study showed that men tend towards aggressive driving behaviors, while women prioritize caution and adherence to traffic rules. Women propensity for safety-oriented features such as lane-keeping assistance, while men display a preference for convenience-focused features such as adaptive cruise control and parking assistance. Conclusion — By addressing challenges and embracing opportunities, the study advocates for a future where ADAS technologies contribute significantly to safer and more equitable roadways for all drivers.

An Analysis Strategic Investment in Smart Mobility and the Impact on Financial Performance of BLUE HYUNDAI

This study explores the strategic investment in smart mobility initiatives by Blue Hyundai and evaluates its impact on the company's financial performance. With the growing demand for innovative, sustainable, and technology-driven transportation solutions, smart mobility has emerged as a critical area of focus for automotive companies. This analysis investigates the relationship between investments in smart mobility technologies—such as electric vehicles (EVs), connected cars, and advanced driver-assistance systems (ADAS)—and key financial performance indicators, including revenue growth, profitability, and market share. The research employs both qualitative and quantitative methods, drawing on financial reports, market trends, and industry data. Findings highlight the potential of smart mobility to enhance operational efficiency, attract environmentally-conscious consumers, and secure long-term competitiveness, while also identifying challenges such as high initial costs and regulatory complexities. Keywords: Smart Mobility, Strategic Investment, Financial Performance, Blue Hyundai, Electric Vehicles, Technology Integration.

Long Short-Term Memory Approach to Predict Battery SOC

Estimating the ‘State of Charge’ (SOC) is a complex endeavour. Data-driven techniques for SOC estimation tend to offer higher prediction accuracy compared to traditional methods. With the progression of Artificial Intelligence (AI), machine learning has found extensive applications across various fields such as infotainment, driver assistance systems, and autonomous vehicles. This paper categorizes the machine learning techniques utilized in Battery Management System (BMS) applications and employs a modern supervised neural network approach to predict SOC. Accurate SOC estimation is crucial to prevent battery failures in critical situations, such as during heavy traffic or when traveling with limited access to charging stations. Long Short-Term Memory (LSTM) networks are particularly adept at classifying, processing, and predicting based on time series data. These models are capable of capturing and retaining features over time, making them suitable for this study. The model's predicted SOC closely matches the true SOC, and the SOC prediction error remains nearly zero even with a large sample of input data.

Theoretical system of levels of constructing vehicle movement of ground urban electric transport vehicle

Digitalization of the transport industry and the use of artificial intelligence technologies in the automation of control of its main processes determine the need to search for new approaches to the description of electrical complexes and systems of transport purpose, their internal and external system properties. The public ground urban electric transport system is considered as a complex process of conversion, transmission and use of electrical energy and information in the electrical complex of urban electric transport, manifested in the organization of controlled movement of vehicles in specific conditions of production tasks and technology of passenger transportation. Based on the synergetic description of the properties, principles of organization and functioning of a complex production and technical system of urban electric transport, the controlled movement of vehicles has been presented as a multilevel technological process implemented by the electrical complex of urban electric transport. It has been proved that the basis of this process, formed by 5 hierarchically arranged levels, each of which provides a separate specific task of building the movement of vehicles, is the implementation of algorithms of traction electrical equipment. A theoretical system of levels of vehicle motion construction as a multilevel process has been formulated, mathematical models of each level have been identified, and sets of conditions characterizing "external" and "internal" "order parameters" defining hierarchical interlevel interrelations have been determined. The results of the work may be of interest to the developers of control systems of autonomous vehicles and driver assistance systems during the selection and justification of algorithms for the effective control of the electrical complex of traction electrical equipment of urban electric transport.

Understanding Individuals’ Continuance Intention to Use Advanced Driver Assistance Systems: An Integrated Application of Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis

This study aimed to understand the factors that influence individuals’ intention to continue using advanced driver assistance systems (ADASs) through an integrated approach that extends the technology acceptance model (TAM). First, perceived safety, perceived quality, and satisfaction were incorporated into the traditional TAM framework as additional constructs to address the complexities of ADAS usage. Second, an approach that combines partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) was employed to identify both the sufficient and necessary conditions for the continuous intention to use ADASs. This combined approach was directed toward data collected from 843 drivers hailing from the Greater Bay Area of China and experienced with ADAS usage. The findings revealed that perceived usefulness, perceived quality, perceived safety, and satisfaction significantly influenced continuance intention, while perceived ease of use indirectly affected it through perceived usefulness and satisfaction. This study underscores the paramount importance of safety and quality perceptions in ADAS adoption and offers practical insights that can help product design and marketing professionals enhance the acceptance and sustained use of ADAS technologies.

Point Cloud Wall Projection for Realistic Road Data Augmentation.

Several approaches have been developed to generate synthetic object points using real LiDAR point cloud data for advanced driver-assistance system (ADAS) applications. The synthetic object points generated from a scene (both the near and distant objects) are essential for several ADAS tasks. However, generating points from distant objects using sparse LiDAR data with precision is still a challenging task. Although there are a few state-of-the-art techniques to generate points from synthetic objects using LiDAR point clouds, limitations such as the need for intense compute power still persist in most cases. This paper suggests a new framework to address these limitations in the existing literature. The proposed framework contains three major modules, namely position determination, object generation, and synthetic annotation. The proposed framework uses a spherical point-tracing method that augments 3D LiDAR distant objects using point cloud object projection with point-wall generation. Also, the pose determination module facilitates scenarios such as platooning carried out by the synthetic object points. Furthermore, the proposed framework improves the ability to describe distant points from synthetic object points using multiple LiDAR systems. The performance of the proposed framework is evaluated on various 3D detection models such as PointPillars, PV-RCNN, and Voxel R-CNN for the KITTI dataset. The results indicate an increase in mAP (mean average precision) by 1.97%, 1.3%, and 0.46% from the original dataset values of 82.23%, 86.72%, and 87.05%, respectively.

Measurement and modelling of driver learning of steering control during successive obstacle avoidance manoeuvres

Understanding of driver behaviour can provide invaluable insight for the design of vehicles and driver assistance systems. Most existing human driver models do not incorporate driver learning or the effect of a driver's confidence in their predictions of future states, both of which affect human-generated control actions. In this paper, human driver steering control is assessed based on experimental data, then a driver model is proposed which captures driver learning and is capable of reproducing a wide range of human control styles by the selection of appropriate parameters. The driver model learns an internal model of the vehicle dynamics from experience, using a Gaussian Process, then selects control actions via Model Predictive Control. The results verify the model's capacity to capture the learning drivers achieve over time and to replicate various observed cautious and adventurous behaviours.

Cameraless sensor fusion: developing a cost-effective driver assistance system using radar and ultrasonic sensor

PurposeThis paper aims to develop a cost-effective, camera-less advanced driver assistance system (ADAS) for electric vehicles. It will use sensor fusion of ultrasonic and radar sensors to implement adaptive cruise control (ACC), blind spot detection (BSD) and reverse parking (RP).Design/methodology/approachThe system was tested on an electric vehicle test bench, using strategically placed ultrasonic and radar sensors. Sensor fusion enabled accurate object detection and distance measurement. The system’s performance was evaluated through simulated obstacle scenarios, with responses monitored via a graphical user interface. Sensor and GPS data were transmitted to the cloud for potential vehicle-to-vehicle communication.FindingsThe sensor fusion approach effectively supported ACC, BSD and RP functions, demonstrating accuracy in obstacle detection, speed adjustment and emergency braking. The real-time system visualization confirmed reliability across various scenarios and cloud integration showed promise for future communication enhancements.Research limitations/implicationsUltrasonic and radar sensors have limited range and accuracy compared to cameras. Ultrasonic sensors are less effective at longer distances and in adverse weather conditions, whereas radar can face challenges in detecting small or stationary objects. Sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar sensors. Sensor performance can be affected by environmental factors such as rain, fog or snow, which may reduce the effectiveness of both ultrasonic and radar sensors.Practical implicationsImproved obstacle detection and collision avoidance contribute to overall vehicle safety. Drivers benefit from advanced features like ACC, BSD and RP without the high cost of traditional camera-based systems. The use of ultrasonic and radar sensors makes advanced driver assistance features more affordable, allowing broader adoption across various vehicle segments, including budget-friendly and mid-range models. The system’s responsiveness and obstacle detection capabilities can lead to more efficient driving, reducing the likelihood of accidents and improving traffic flow.Social implicationsEnhanced safety features such as ACC, BSD and RP contribute to reducing traffic accidents and injuries. By making advanced driver assistance features more affordable, the system improves vehicle safety for a broader range of drivers, including those in lower-income brackets. The introduction of such systems can raise public awareness about the benefits of ADAS technologies and their role in enhancing road safety.Originality/valueThis study introduces a novel ADAS system that eliminates the need for cameras by leveraging the strengths of radar and ultrasonic sensors. The approach offers a practical and innovative solution for enhancing vehicle safety at a reduced cost.

Lightweight Neural Network Model and Algorithm for Pedestrian Detection

<div>Traditional pedestrian detection methods have poor robustness. Deep learning-based methods have shown high performance in recent years but rely on substantial computational resources. Developing a lightweight, deep learning-based pedestrian detection algorithm is essential for applying deep learning-based algorithms in resource-limited scenarios, such as driverless and advanced driver assistance systems. In this article, an improved model based on YOLOv3 called “YOLOPD” (You Only Look Once—Pedestrian Detection), is proposed. It is obtained by constructing a self-attentive module, introducing a CIOU (Complete Intersection over Union) loss function and a depth separated convolutional layer. Experimental results show that on the INRIA (National Institute for Research in Computer Science and Automation), Caltech, and CityPerson pedestrian dataset, the MR (miss rate) of the model YOLOPD is better than that of the original YOLOv3 model, and the number of parameters is reduced by about 1/3, which significantly improves the speed of network derivation while improving detection accuracy.</div>

Cooperation Dynamics in Multiagent Systems: Modeling Vehicular Cooperation through Game Theory

<div>Cooperation lies at the core of multiagent systems (MAS) and multiagent reinforcement learning (MARL), where agents must navigate between individual interests and collective benefits. Advanced driver assistance systems (ADAS), like collision avoidance systems and adaptive cruise control, exemplify agents striving to optimize personal and collective outcomes in multiagent environments. The study focuses on strategies aimed at fostering cooperation with the aid of game-theoretic scenarios, particularly the iterated prisoner’s dilemma, where agents aim to optimize personal and group outcomes. Existing cooperative strategies, such as tit-for-tat and win-stay lose-shift, while effective in certain contexts, often struggle with scalability and adaptability in dynamic, large-scale environments. The research investigates these limitations and proposes modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. By analyzing existing cooperative strategies, the research investigates their effectiveness in encouraging group-oriented behavior in repeated games. It suggests modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. Furthermore, it extends to scenarios with exponentially growing agent populations (<i>N</i> → +∞), addressing computational challenges using mean-field game theory to establish equilibrium solutions and reward structures tailored for infinitely large agent sets. Practical insights are provided by adapting simulation algorithms to create scenarios conducive to cooperation for group rewards. Additionally, the research advocates for incorporating vehicular behavior as a metric to assess the induction of cooperation, bridging theoretical constructs with real-world applications.</div>

Exploring the influential factors of initial trust in autonomous cars

Initial trust is one of the critical factors that influence the acceptance of and reliance on autonomous cars (ACs). This study identified the determinants of the initial trust in ACs and explored the relationships between them using structural equation modelling. A survey was conducted using a questionnaire to obtain demographic information, personality traits, design features, task scenarios, and human perception factors from 101 participants without prior interactions with ACs. The results showed that the perceived safety (0.716), capability (0.222), and external locus of control (0.101) are the main positive factors fostering initial trust in ACs, while task risk (-0.349) was the main negative factor. Multigroup analysis demonstrated that the respondents’ previous experience with driver-assistance systems encouraged the development of initial trust. The results of this study can provide guidelines for the design and promotion of ACs to develop individuals’ initial trust in ACs.

The Optimal Solution for Vehicles Navigation Systems through Sensor Fusion

This article discusses the evolution of vehicle navigation systems, highlighting the limitations of traditional GPS systems, especially in challenging environments such as urban canyons or tunnels. Sensor fusion, the integration of data from multiple sensors such as LiDAR, cameras, and inertial measurement units (IMUs), is a powerful alternative. By combining the advantages of these different sensors, sensor fusion improves the accuracy, reliability, and safety of vehicle navigation, especially for autonomous vehicles and advanced driver assistance systems (ADAS). The study suggests various sensors, data integration algorithms, and potential improvements to navigation systems. The goal of multi-sensor fusion in-vehicle navigation is to combine data collected from a set of sensors to improve the quality of the solution. Different types of sensors provide physical properties. Different fusions are used to directly integrate sensor data to obtain parameters, while other fusions are used to indirectly integrate sensor data in a layered vehicle to obtain control signals suggested by commands from different modules.