Advanced Driver Assistance Systems Research Articles

Impact of Handedness on Driver’s Situation Awareness When Driving under Unfamiliar Traffic Regulations

Situation awareness (SA) describes an individual’s understanding of their surroundings and actions in the near future based on the individual’s comprehension and understanding of the surrounding inputs. SA measurements can be applied to improve system performance or human effectiveness in many fields of study, including driving. However, in some scenarios drivers might need to drive in unfamiliar traffic regulations (UFTRs), where the traffic rules and vehicle configurations are a bit different from what the drivers are used to under familiar traffic regulations. Such driving conditions require drivers to adapt their attention, knowledge, and reactions to safely reach the destination. This ability is influenced by the degree of handedness. In such tasks, mixed-/left-handed people show better performance than strong right-handed people. This paper aims to explore the influence of the degree of handedness on SA when driving under UFTRs. We analyzed the SA of two groups of drivers: strong right-handed drivers and mixed-/left-handed drivers. Both groups were not familiar with driving in keep-left traffic regulations. Using a driving simulator, all participants drove in a simulated keep-left traffic system. The participants’ SA was measured using a subjective assessment, named the Participant Situation Awareness Questionnaire PSAQ, and performance-based assessment. The results of the study indicate that mixed-/left-handed participants had significantly higher SA than strong right-handed participants when measured by performance-based assessment. Also, in the subjective assessment, mixed-/left-handed participants had significantly higher PSAQ performance scores than strong right-handed participants. The findings of this study suggest that advanced driver assistance systems (ADAS), which show improvement in road safety, should adapt the system functionality based on the driver’s degree of handedness when driving under UFTRs.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Impacts of information quantity and display formats on driving behaviors in a connected vehicle environment

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human–machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers’ responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers’ gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs.

Sustainable edge computing: Challenges and future directions

AbstractThe advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Investigation of Automotive LiDAR Vision in Rain from Material and Optical Perspectives

With the emergence of autonomous functions in road vehicles, there has been increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor, detecting the positions of obstacles by representing them as clusters of points in three-dimensional space. LiDAR performance degrades significantly when a vehicle is driving in the rain as raindrops adhere to the outer surface of the sensor assembly. Performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradation is highly dependent on the interface material properties. This subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of a LiDAR assembly coated with four classes of material—hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to the different droplets of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis. The results are validated with physical optical tests, using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations of LiDAR performance degradation in rain are presented from both material and optical perspectives. These can aid component selection and the development of signal-enhancing strategies for the integration of LiDARs into vehicle designs to minimize the impact of rain.

Monitoring Fuel-Efficient Driving Patterns to Augment ADAS to regulate the fuel dynamically using Machine Learning

In the realm of enhancing Advanced Driver-Assistance Systems (ADAS) for optimal vehicle performance and sustainability, this paper introduces an innovative methodology that capitalizes on Machine Learning (ML) to scrutinize and learn from fuel-efficient driving patterns. Utilizing real-time driving cycle datasets collected through On-Board Diagnostics (OBD) tools, meticulously recorded the driving styles of 100 drivers on a predetermined route over duration of 35 minutes. The rich dataset includes an array of vehicle parameters and driving behavior, providing a comprehensive foundation for ML-based analysis. Proposed approach involves the application of state-of-the-art ML algorithms, with a specific focus on Machine learning and ensemble methods, to accurately model and predict fuel-efficient driving patterns. The algorithms XGB-regression, Random Forest regression and Gradient Boosting regressions were rigorously trained, validated, and tested on the collected data, ensuring robustness and reliability in their predictive capabilities. The scores and the Mean absolute Errors of these algorithms were estimated, most efficient algorithms are RF-regression and GB-regressions with a score of 99.93% and 99.93% with MAE 0.01219% and 0.01210%respectively. The integration of these predictive models into the ADAS framework mayshow promising results, significantly improving the system’s performance in real-time decision-making and driver assistance. The system demonstrates an enhanced capability to adapt to varying driving styles, offering personalized recommendations for fuel-efficient driving, and contributing to the reduction of fuel consumption and emissions.

Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

How do Car Owners Familiarize and Adapt to Unfamiliar Vehicle Technologies?

Many drivers lack relevant knowledge about advanced driver assistance systems (ADAS), and how they learn about ADAS is mainly haphazard. We conducted interviews with six experts from the automobile industry and eight drivers. Few drivers actively engage with ADAS features and settings in their vehicles and many fail to check or familiarize themselves with ADAS in an unfamiliar car. Moreover, the research highlights that it is not rare for drivers to misinterpret typical ADAS responses as technical issues.

Anti-Low Angle Resolution: Some adaptive improvements in anchor-based object detection algorithm for automotive radar target detection

Automotive radar has extensive and well-established applications in advanced driver assistance systems (ADAS). It has been the most reliable and preferred sensor for functions such as adaptive cruise control and automatic emergency braking. Nevertheless, the limited angle resolution of traditional millimeter-wave radar data reduces its effectiveness in autonomous driving systems (ADS). Our study presents some adaptive improvements in anchor-based object detection algorithm framework to address the inaccuracies in target detection and positioning resulting from the limited angle resolution of millimeter-wave radar data. Our model utilizes the Range-Angle (RA) and Range-Doppler (RD) views of a RAD cube as inputs. An anchor assignment strategy - Max Intersection over Union (IoU) assigner is used to generate more positive samples than before, enabling the model to learn more about the relevant features of targets. We propose a multi-detection box fusion Non-Maximum Suppression (NMS) mechanism to fuse prediction results with varying confidence scores, thus improving the accuracy of target detection positions by addressing the issue of target box position deviation caused by low angle resolution. In addition, an actual distance loss function is designed to constrain the regularity that the size of the bounding box is inversely proportional to the distance between the target and the radar. Based on experimental results, the proposed method increases the mean average precision (mAP) by approximately 10% compared to the baseline method.

Lane Line Detection

Abstract: Lane line detection is a crucial component in the development of advanced driver assistance systems (ADAS) and autonomous vehicles. This research proposes a comprehensive approach to enhance the accuracy and robustness of lane line detection algorithms, addressing challenges such as varying road conditions, lighting conditions, and diverse road markings.The proposed methodology leverages computer vision techniques, including edge detection, color space transformations, and image filtering, to preprocess input images from onboard cameras. A novel algorithm for lane line extraction is introduced, combining feature extraction and geometric analysis to accurately identify lane markings. The algorithm adapts to different road scenarios by dynamically adjusting parameters based on environmental conditions. The proposed lane line detection system demonstrates promising results in terms of accuracy, adaptability, and real-time performance, showcasing its potential for practical implementation in autonomous vehicles and ADAS. The research contributes to the ongoing efforts in creating robust and reliable perception systems for safe and efficient autonomous navigation on roads.

Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter

An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.

Towards a Model of Snow Accretion for Autonomous Vehicles

Towards a Model of Snow Accretion for Autonomous Vehicles

Lane detection networks based on deep neural networks and temporal information

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes.

Advanced Driver Assistance System (ADAS)

The motivations are art, elegance, and ethics. Human error and negligence cause 90% of road accidents, making the human the important part in this process while also being the weakest link. Every year, numerous accidents are reported as a result of excessive speed and poor decision-making. According to a logical concept, every 1% increase in velocity increases the probability of a fatal collision by 4% and the risk of a serious collision by 3%. Modern automobiles are a symbiosis of several electrical subsystems that work together to provide a safe and enjoyable driving experience. One of the technologies used in automotive development is the Advanced Driver Assistant System (ADAS). It is a system that monitors different parameters related to the vehicle and its surroundings in order to detect potentially dangerous circumstances early on. Sensors with sophisticated electronics, known as Sensor Network (SN), are already installed in many cars today to accomplish (ADAS) technology. A well-known ADAS is the Adaptive Speed Control system (ASC), Automatic Brake System (ABS), Warning Collision System (WCS), and Legal Restriction System, which are used to avert high-speed accidents. In addition, there are Lane Keeping Systems (LKS) and Lane Change Systems (LCS).

Semi-supervised vanishing point detection with contrastive learning

Vanishing point (VP) detection in road images plays an important role in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Because it is still a challenging problem to obtain expensive annotated datasets in deep-learning-based supervised training, in this paper, we propose a semi-supervised VP detection method in road images. Our proposed model first extracts high-resolution VP heatmaps of the road images by fusing multi-level and global–local contrastive learning. Then, we apply the π model as a semi-supervised learning framework and feed the whole global network with a small number of training samples to detect VPs. In the experiment, we used Kong’s dataset for the unstructured road test and the KITTI-VP dataset for the structured road test. Compared with the state-of-the-art methods, our model achieved high accuracy and robustness in road VP detection.

Minimizing optical attribute errors for a lane departure warning system using an ultra-wide-angle camera

Advanced driver assistance systems (ADAS) rely on lane departure warning (LDW) technology to enhance safety while driving. However, the current LDW method is limited to cameras with standard angles of view, such as mono cameras and black boxes. In recent times, more cameras with ultra-wide-angle lenses are being used to save money and improve accuracy. However, this has led to some challenges such as fixing optical distortion, making the camera process images faster, and ensuring its performance. To effectively implement LDW, we developed three technologies: (i) distortion correction using error functions based on the projection characteristics of optical lenses, (ii) automatic vanishing point estimation using geometric characteristics, and (iii) lane tracking and lane departure detection using constraints. The proposed technology improves system stability and convenience through automatic calculation and updating of parameters required for LDW function operation. By performing automatic distortion correction and vanishing point estimation, it has also been proven that fusion with other ADAS systems including front cameras is possible. Existing systems that use vanishing point information do not consider lens distortion and have slow and inaccurate vanishing point estimation, leading to a deterioration of system performance. The proposed method enables fast and accurate vanishing point estimation, allowing for adaptive responses to changes in the road environment.

Real-world crash configurations and traffic violations among newly licensed young drivers with different route familiarity levels

Objective Young drivers aged 24 and below are at heightened risks of being influenced by their route familiarity levels. This study aims to compare prevalences of crash culpability, crash configurations and risky driver behaviors among newly licensed young drivers when they are driving on roads with different route familiarity levels. Methods Based on the road traffic crash and violation data in Yunnan Province of China from January 2017 through December 2019, we classified drivers’ different route familiarity levels by utilizing spatial distance away from residence-based method, including driving on high route familiarity (HRF) and low route familiarity (LRF) roads. Prevalence ratios were estimated using generalized estimating equation log-binomial regression models. Results We identified 12016 newly licensed young drivers driving on HRF roads and 2189 drivers on LRF roads. Within 48 months of licensure, young drivers on LRF roads were more likely to be at fault for their motor vehicle crashes than those on HRF roads. Young drivers on LRF roads were more likely to be with failure to obey traffic control device, with failure to yield right of way, wrong way driving, backing unsafely and improper parking compared with those on HRF roads. Drivers on LRF roads were less likely to be inattentive and driving with unsafe speed and following too closely compared with those on HRF roads. Conclusions Several basic aspects of targeted countermeasures can be put forward. Visual impacts such as rectangular rapid-flashing beacon (RRFB) can be used in order to prevent wrong way driving on the tourist roadways. Arranging safety talks and programs in colleges and universities and technical interventions like Advanced Driver Assistance Systems (ADAS) can be used to reduce young drivers’ driving distraction and overconfidence. It is recommended that the driving schools can use these research findings to include in licensure program to make young drivers more aware of the various factors that expose them to crash risks so that more defensive driving may be needed under different situations, and this can also help build the graduated driving licensure (GDL) programme in China.

Eco-Driving on Hilly Roads in a Mixed Traffic Environment: A Model Predictive Control Approach

Human driving behavior significantly affects vehicle fuel economy and emissions on hilly roads. This paper presents an ecological (eco) driving scheme (EDS) on hilly roads using nonlinear model predictive control (NMPC) in a mixed traffic environment. A nonlinear optimization problem with a relevant prediction horizon and a cost function is formulated using variables impacting the fuel economy of vehicles. The EDS minimizes vehicle fuel usage and emissions by generating the optimum velocity trajectory considering the longitudinal motion dynamics, the preceding vehicle’s state, and slope information from the digital road map. Furthermore, the immediate vehicle velocity and angle of the road slope are used to tune the cost function’s weight utilizing fuzzy inference methods for smooth maneuvering on slopes. Microscopic traffic simulations are used to show the effectiveness of the proposed EDS for different penetration rates on a real hilly road in Fukuoka City, Japan, in a mixed traffic environment with the conventional (human-based) driving scheme (CDS). The results show that the fuel consumption and emissions of vehicles are significantly reduced by the proposed NMPC-based EDS compared to the CDS for varying penetration rates. Additionally, the proposed EDS significantly increases the average speed of vehicles on the hilly road. The proposed scheme can be deployed as an advanced driver assistance system (ADAS).

Driving Towards Safety: The Role of ECUs and IMUs in Advanced Driver-Assistance Systems (ADAS)

This research paper explores the pivotal role of Electronic Control Units (ECUs) and Inertial Measurement Units (IMUs) in Advanced Driver-Assistance Systems (ADAS), focusing on their functionalities, applications, regulatory frameworks, emerging technologies, and real-world case studies. Through a comprehensive review of literature, this paper investigates the integration of ECUs and IMUs in ADAS and its implications for vehicle safety, mobility, and sustainability. Key topics addressed include the adoption challenges, societal impact, international regulatory landscape, standardization efforts, emerging technologies such as artificial intelligence and sensor fusion, and real case studies exemplified by Tesla's Autopilot system, Volvo's City Safety system, and Waymo's autonomous taxi service. The paper concludes by highlighting the transformative potential of ECUs and IMUs in shaping the future of automotive technology and advancing towards safer, more efficient, and more sustainable transportation systems.

Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter.

Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA(2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception.