Advanced Driver Assistance Systems Research Articles

Phase transition in heterogeneous macro continuum model integrating the jerk effect with the mixed traffic flow involving human driving and ADAS vehicles

Vehicles equipped with advanced driving assistance systems (ADAS) can acquire information about preceding and following vehicles, which will have an undeniable impact on the phase transition of traffic flow. Therefore, we establish a new heterogeneous continuum model of traffic flow considering the jerk effect mixed with human driving (HD) and ADAS vehicles. The neutral stability condition associated with the permeability of ADAS vehicle is inferred via stability analysis. According to simulation, shock wave occurs, implying that our model can emerge correct wave propagation trend. Moreover, the simulation of density evolution and flow changes also suggests that an increase in ADAS vehicle penetration and jerk effect can enhance traffic system stability and reduce energy consumption.

Correlation Between Traffic Locus of Control and Advanced Driver Assistance System (ADAS) Use

Inappropriate use of advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC) and lane keeping assistance (LKA), may cause safety issues. In a driving simulator experiment, we examined how a specific driver characteristic, locus of control in traffic (T-LOC), influences the use of ADAS. It was found that without any ADAS training, the Self dimension of T-LOC, measuring the extent to which one believes they are responsible for their driving safety, was negatively correlated with time spent on secondary tasks after environmental cues became visible indicating a possible need for takeover. Further, with a responsibility-focused training, drivers with higher Self scores tended to take control over faster when necessary. The findings highlight the role of individual differences in the formation of driver behaviors associated with ADAS use. Investigation of other driver characteristics and incorporating them to the design of training paradigms are suggested.

Predictive Models of Brake Reaction and Steering of Police Officers in Critical Driving Situations

Motor vehicle crashes involving police vehicles significantly impact law enforcement officer (LEO) safety, often occurring during high-risk maneuvers like pursuit driving and sudden lane changes. These incidents are exacerbated by LEOs’ interaction with in-vehicle technologies, including Mobile Computer Terminals. Advanced Driver-Assistance Systems (ADAS) could mitigate these risks, though existing ADAS algorithms are not tailored to the unique work demands. To address these challenges, our study develops predictive models for LEO braking and steering in critical situations, based on a driving simulator experiment with police officers. Both braking and steering models were constructed and identified as effective in predicting police officer braking response and steering. The models can be practically applied as part of ADAS systems to deliver warnings at appropriate times, adapting to police officer demands in critical driving situations. Study implications contribute to the wider body of knowledge on surface transportation, occupational safety, and the use of adaptive models.

Organizing planning knowledge for automated vehicles and intelligent transportation systems

AbstractIntelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision‐making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high‐level road plan. To this end, this research compares cutting‐edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real‐time results have been obtained for fast KNN algorithms, leading to a robust tree index‐based methodology for decision making in self‐driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.

SMALL OBJECT DETECTION APPROACH BASED ON ENHANCED SINGLE-SHOT DETECTOR FOR DETECTION AND RECOGNITION OF INDONESIAN TRAFFIC SIGNS

The detection and recognition of traffic signs are crucial components of advanced driving assistance systems (ADAS) that enhance road safety. Current traffic sign detection and recognition model technology is proficient in identifying and interpreting traffic signs. However, for accurate detection and recognition, the traffic sign in the image must be of a certain minimum pixel size or distance from the driver's sight line for proper detection. The ADAS system should be capable of detecting and recognizing road traffic signs from a considerable distance as they come into the driver's line of vision. The higher the vehicle speed, the greater the distance required for the sign to be detected and recognized, allowing the driver sufficient time to react according to the sign's meaning. Addressing these challenges, this research proposes an enhanced version of the single shot detector (SSD) algorithm, commonly used in object detection, to improve the algorithm's ability to detect small objects. The proposed method involves adding an auxiliary layer module to the original SSD architecture to increase the feature map resolution and expand the conventional layer's receptive space. With the Enhanced SSD algorithm, the detection capability of the SSD can be significantly enhanced in terms of accuracy. The limitations of this study are related to the influence of occlusion and clutter, which might affect the performance of object detection, especially for small objects, which are more susceptible to being influenced by various factors. The research results demonstrate that Enhanced SSD improves object detection accuracy compared to the original SSD, with a mean average precision (mAP) of 97.87 compared to 95.35 for detecting 21 traffic signs in Indonesia.

Safety Trends in the Automobile Industry in India

The automobile industry in India has experienced significant advancements in recent years, driven by rapid technological innovations and evolving consumer expectations. Safety has emerged as a critical focus area, with stakeholders ranging from government bodies to manufacturers and consumers increasingly emphasizing the need for stringent safety measures. This paper explores the key safety trends shaping the Indian automotive sector, including the adoption of advanced driver-assistance systems (ADAS), the implementation of new crash test standards, and the increased integration of passive and active safety features in vehicles. Additionally, it highlights the regulatory changes brought forth by the Indian government, such as the Bharat New Vehicle Safety Assessment Program (BNVSAP) and mandatory safety norms like airbags and anti-lock braking systems (ABS). The study also examines the role of technological innovations, including connected vehicle technologies and the potential of autonomous driving in enhancing road safety. By analyzing these trends, the paper aims to provide insights into how safety measures in the Indian automobile industry are evolving to meet global standards and reduce the rising number of road accidents in the country.

Depth Video-Based Secondary Action Recognition in Vehicles via Convolutional Neural Network and Bidirectional Long Short-Term Memory with Spatial Enhanced Attention Mechanism

Secondary actions in vehicles are activities that drivers engage in while driving that are not directly related to the primary task of operating the vehicle. Secondary Action Recognition (SAR) in drivers is vital for enhancing road safety and minimizing accidents related to distracted driving. It also plays an important part in modern car driving systems such as Advanced Driving Assistance Systems (ADASs), as it helps identify distractions and predict the driver’s intent. Traditional methods of action recognition in vehicles mostly rely on RGB videos, which can be significantly impacted by external conditions such as low light levels. In this research, we introduce a novel method for SAR. Our approach utilizes depth-video data obtained from a depth sensor located in a vehicle. Our methodology leverages the Convolutional Neural Network (CNN), which is enhanced by the Spatial Enhanced Attention Mechanism (SEAM) and combined with Bidirectional Long Short-Term Memory (Bi-LSTM) networks. This method significantly enhances action recognition ability in depth videos by improving both the spatial and temporal aspects. We conduct experiments using K-fold cross validation, and the experimental results show that on the public benchmark dataset Drive&Act, our proposed method shows significant improvement in SAR compared to the state-of-the-art methods, reaching an accuracy of about 84% in SAR in depth videos.

Simulation of Advanced Driving Assistance Systems for a Dynamic Vehicle Model

Simulation of Advanced Driving Assistance Systems for a Dynamic Vehicle Model

Road target recognition based on radar range-Doppler spectrum with GS – ResNet

ABSTRACT Road target recognition ability of automotive millimetre-wave (mmWave) radar is crucial in areas such as autonomous driving, advanced driver assistance systems (ADAS), and automated emergency braking systems (AEBS). Current mmWave radar systems are primarily utilized for speed, distance, and angle measurements, with limited capability or unsatisfactory performance in target classification. In response to this limitation, this paper proposes a novel method for road target recognition, using radar range-Doppler spectrum as input data and introducing a global attention mechanism and SCConv module. First, the range-Doppler spectrum is processed by the clutter removal algorithm block to remove background clutter and vibration interference. Second, considering the small features and susceptibility to noise interference in the range-Doppler spectrum, we have devised a deep residual network based on a global attention mechanism, significantly enhancing the model’s accuracy in range-Doppler spectrum classification. Finally, we introduce the SCConv module and improve the downsampling module in ResNet to enhance the model’s classification accuracy. Experimental results demonstrate that the model achieves an average accuracy of 99.79% in classifying six types of road targets, significantly outperforming other methods. This research is of significant importance in advancing the understanding of road traffic conditions by autonomous driving systems and enhancing system safety.

Enhancing Autonomous Driving Safety: A Robust Stacking Ensemble Model for Traffic Sign Detection and Recognition

Accurate detection and classification of traffic signs play a vital role in ensuring driver safety and supporting advancements in autonomous driving technology. This paper introduces a novel approach for traffic sign detection and recognition by integrating the Faster RCNN and YOLOX-Tiny models using a stacking ensemble technique. The innovative ensemble methodology creatively merges the strengths of both models, surpassing the limitations of individual algorithms and achieving superior performance in challenging real-world scenarios. The proposed model was evaluated on the CCTSDB dataset and the MTSD dataset, demonstrating competitive performance compared to traditional algorithms. All experiments were conducted using Python 3.8 on the same system equipped with an NVIDIA GTX 3060 12G graphics card. Our results show improved accuracy and efficiency in recognizing traffic signs in various real-world scenarios, including distant, close, complex, moderate, and simple settings, achieving a 4.78% increase in mean Average Precision (mAP) compared to Faster RCNN and improving Frames Per Second (FPS) by 8.1% and mAP by 6.18% compared to YOLOX-Tiny. Moreover, the proposed model exhibited notable precision in challenging scenarios such as ultra-long-distance detections, shadow occlusions, motion blur, and complex environments with diverse sign categories. These findings not only showcase the model’s robustness but also serve as a cornerstone in propelling the evolution of autonomous driving technology and sustainable development of future transportation. The results presented in this paper could potentially be integrated into advanced driver-assistance systems and autonomous vehicles, offering a significant step forward in enhancing road safety and traffic management.

Securing the vetaverse: Web 3.0 for decentralized Digital Twin-enhanced vehicle–road safety

The rapid evolution of vehicular communication technologies in recent times necessitates robust security measures and enhanced road safety protocols. Integrity of data shared between vehicles, their Digital Twins (DT) and road side units is at stake. Intrusion in these data can potentially lead to misinformation in Advanced Driving Assistance Systems (ADAS) causing serious consequences upon road safety. These include improper detection of drunk driving behaviors. In this domain, Web 3.0 emerges as the overarching approach that can transform security of vehicles and ensure road safety. This paper explores the potential of Web 3.0 and its key enabling technologies to establish a VEhicular meTAVERSE (Vetaverse) utilizing edge-based DTs of the vehicles to process their dynamics shared in real-time, and based on its deep learning models, predict whether the driving behavior is drunk or sober. This Deep Neural Network (DNN) performs these predictions with 96% accuracy. It secures all Vehicle-to-Digital Twin (V2DT) communications via Multichain - a horizontally scaled parallel blockchains platform that tamper proofs each bit of sensor data, and optimizes transaction validation time to leverage vetaverse security. Results reveal that this framework is accurate and computationally lightweight in comparison to existing state-of-the-art, and brings Web 3.0 to the crucial road safety use-case.

Trajectory representation learning with multilevel attention for driver identification

Massive trajectory data have originated from the development of positioning technology. Learning GPS trajectory representation to characterize a driver’s driving style is a challenging task with important applications in many areas, including autonomous driving, auto insurance, advanced driver assistance systems, urban computing, and the internet of things. Few studies have considered the interactions between different factors. In this study, we propose a novel trajectory representation method based on a multilevel attention mechanism (ATTraj2vec) and apply it to the task of driver identification. We use 1D CNN to summarize local features, including motion, spatial and temporal features. Then we utilize a multilevel attention mechanism to extract global features, aggregating the interactions of motion features with temporal and spatial features progressively. Additionally, we adopt multi-loss to optimize our model simultaneously, which consists of a softmax loss for driver classification and Siamese loss for making trajectories from the same driver more similar. Classification experimental results on a real-world automobile trajectory dataset demonstrated that our proposed model significantly outperforms existing baselines. Meanwhile, the proposed method provides significant gains in the trajectory clustering of unseen drivers. The code is available in the repository, https://github.com/limengyuan2021/ATTraj2vec,

Stability of Adaptive Cruise Control of Automated Vehicle Platoon under Constant Time Headway Policy

Traffic congestion is becoming more prevalent as the number of vehicles on the roads continues to rise.To shorten travel times and enhance driver comfort, a range of Advanced Driver Assistance Systems (ADAS) has been developed to assist drivers in urban areas and on highways. The demand for increasing road capacity has introduced the concept of vehicle platooning, where the use of the Adaptive Cruise Control (ACC) system is a key function within the advanced driver assistance (ADAS) technology, this technology manages the vehicle's longitu-dinal control in specific driving conditions. Cars equipped with ACC can efficiently maintain a set distance from the vehicle ahead, easing the driver’s workload while offering advantages such as improved road capacity, lower fuel consumption, and reduced pollution emissions. However, they can be susceptible to string instability, resulting in the amplification of oscillations caused by speed variations along the platoon's rear. This paper presents a string stability analysis of car platoons equipped with ACC system based on a heuristic method by choosing of the constant time headway policy (CTHP). The constant time headway policy selection for string stability is based on the Nyquist diagram of the transfer function of the spacing errors between two cars. A platoon operated by using distance-based ACC control structures is implemented. These structures employ a linear quadratic regulator (LQR) using a dual integrator. The simulation results were acquired by modeling and simulating the studied platoon within Matlab/Simulink.

REVOLUTIONIZING MOBILITY: A COMPREHENSIVE EVALUATION OF THE IMPACT OF AI ON AUTOMATED VEHICLES

Automated vehicles (AVs) represent a game-changing advancement in transportation, utilizing cutting-edge technologies to travel without human intervention. These vehicles, which range from advanced driver assistance systems (ADAS) to completely autonomous versions, rely on artificial intelligence (AI) to perceive and make decisions. Recent advances in sensor technology and machine learning have improved AV capabilities, but understanding how AI responds to various distance metrics is critical for assuring safe journeys. Hypothesizing that the more the distance the car is traveling, the more AI may be able to provide a safe journey for the passengers,this study seeks to investigate the complex relationship between distance and AIs ability to ensure AV safety. Investigating the physical, geographical, and temporal characteristics of distance can help optimize AV performance and reduce safety issues.

A novel system architecture of intelligent adaptive cruise control for safety aspects

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

The Impact of Advanced Driver-Assistance Systems (ADAS) on Road Safety in Nigeria

This study investigates the impact of Advanced Driver-Assistance Systems (ADAS) on road safety in Nigeria, focusing on their effectiveness in reducing traffic accidents, public awareness, and the challenges hindering their adoption. Utilizing a descriptive survey research design, data were collected from 310 respondents, including administrators, teachers, and students across various Government Technical Colleges in Adamawa State. The study revealed a strong positive correlation between the availability of ADAS and the reduction of road traffic accidents (r = 0.65, p = 0.002) and a significant association between public awareness of ADAS and safer driving behaviors (r = 0.60, p = 0.003). However, economic and infrastructural challenges were identified as significant barriers to the adoption of these technologies (r = -0.70, p = 0.001). Based on the findings, it is recommended that comprehensive public awareness campaigns be implemented to educate drivers on ADAS benefits, government financial incentives be introduced to encourage the purchase of ADAS-equipped vehicles, investments be made to improve road infrastructure, and research and development initiatives be fostered to create cost-effective ADAS solutions tailored to Nigeria’s driving conditions. Addressing these challenges and implementing these recommendations can lead to enhanced road safety and reduced accident rates, making it essential for stakeholders to collaborate in fostering a safer driving environment.

Marking-Based Perpendicular Parking Slot Detection Algorithm Using LiDAR Sensors

The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions.

Analysis of Advanced Driver-Assistance Systems for Safe and Comfortable Driving of Motor Vehicles.

This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of this document describes the sensory detectors used in ADASs, including radars, cameras, LiDAR, and ultrasonics. The subsequent chapter presents the most popular driver assistance systems, including adaptive cruise control (ACC), blind spot detection (BSD), lane keeping systems (LDW/LKS), intelligent headlamp control (IHC), and emergency brake assist (EBA). A key element of this work is the evaluation of the effectiveness of these systems in terms of safety and driving comfort, employing a survey conducted among drivers. Data analysis illustrates how these systems are perceived and identified areas requiring improvements. Overall, the paper shows drivers' positive reception of ADASs, with most respondents confirming that these technologies increase their sense of safety and driving comfort. These systems prove to be particularly helpful in avoiding accidents and hazardous situations. However, there is a need for their further development, especially in terms of increasing their precision, reducing false alarms, and improving the user interface. ADASs significantly contribute to enhancing safety and driving comfort. Yet, they are still in development and require continuous optimization and driver education to fully harness their potential. Technological advancements are expected to make these systems even more effective and user-friendly.

An in-vehicle real-time infrared object detection system based on deep learning with resource-constrained hardware

Advanced driver assistance systems primarily rely on visible images for information. However, in low-visibility weather conditions, such as heavy rain or fog, visible images struggle to capture road conditions accurately. In contrast, infrared (IR) images can overcome this limitation, providing reliable information regardless of external lighting. Addressing this problem, we propose an in-vehicle IR object detection system. We optimize the you only look once (YOLO) v4 object detection algorithm by replacing its original backbone with MobileNetV3, a lightweight feature extraction network, resulting in the MobileNetV3-YOLOv4 model. Furthermore, we replace traditional pre-processing methods with an Image Enhancement Conditional Generative Adversarial Network inversion algorithm to enhance the pre-processing of the input IR images. Finally, we deploy the model on the Jetson Nano, an edge device with constrained hardware resources. Our proposed method achieves an 82.7% mean Average Precision and a frame rate of 55.9 frames per second on the FLIR dataset, surpassing state-of-the-art methods. The experimental results confirm that our approach provides outstanding real-time detection performance while maintaining high precision.

Assessing advanced driver assistance systems in police vehicles under demanding conditions

Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers’ safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers’ driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers’ driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.