Search Results (157)

This paper details the design and implementation of a fuzzy logic control system for an advanced driver-assistance system (ADAS) that adjusts brake force based on proximity sensing, vehicle speed, and road conditions. By employing a cost-effective ultrasonic sensor (HC-SR04) and an STM32 microcontroller, the system facilitates real-time adjustments to braking force, enhancing both vehicle safety and driver comfort. The fuzzy logic controller processes three inputs to deliver a smooth and adaptive brake response, thus addressing the shortcomings of traditional binary systems that can lead to abrupt and unsafe braking actions. The effectiveness of the system is validated through several test cases, demonstrating improved responsiveness and safety across various driving scenarios. This paper presents a cost-effective model for a straightforward braking system using fuzzy logic, laying the groundwork for the development of more advanced systems in emerging technologies. Full article

Computer vision is one of the key technologies of advanced driver assistance systems (ADAS), but the incorporation of a vision-based driver assistance system (still) poses a great challenge due to the special characteristics of the algorithms, the neural network architecture, the constraints, and the strict hardware/software requirements that need to be met. The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. Results indicate that a resolution of 512 × 256 yields the best trade-off, offering high mIoU and F1 scores while maintaining real-time processing speeds on a standard CPU. A key contribution of this work is the demonstration that combining synthetic and real datasets enhances model performance, especially when real data is limited. The novelty of this study lies in its dual analysis of simulation-based data and image resolution as key factors in training effective lane detection systems. These findings support the use of synthetic environments in training neural networks for autonomous driving applications. Full article

Advanced driving assistance systems (ADASs) provide rich data on vehicles and their surroundings, enabling early detection and warning of driving risks. This study proposes a short-term risk prediction method based on in-vehicle perception data, aiming to support real-time risk identification in ADAS environments. A variable sliding window approach is employed to determine the optimal prediction window lead length and duration. The method incorporates Monte Carlo simulation for threshold calibration, Boruta-based feature selection, and multiple machine learning models, including the light gradient-boosting machine (LGBM), with performance interpretation via SHAP analysis. Validation is conducted using data from 90 real-world driving sessions. Results show that the optimal prediction lead time and window length are 1.6 s and 1.2 s, respectively, with LGBM achieving the best predictive performance. Risk prediction effectiveness is enhanced when integrating information across the human–vehicle–road environment system. Key features influencing prediction include vehicle speed, accelerator operation, braking deceleration, and the reciprocal of time to collision (TTCi). The proposed approach provides an effective solution for short-term risk prediction and offers algorithmic support for future ADAS applications. Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

Advanced driving assistance systems (ADASs) are critical for automotive safety. They rely on various sensors (especially with an increasing reliance on visual sensors to meet evolving safety standards) to capture relevant environmental data. The validation of ADAS systems is crucial to ensure their reliability and performance in real-world driving scenarios; however, this requires reference data. This paper focuses on the development of a reference sensor system that can provide reference data and does support the validation of visual sensors for ADAS systems. The system is validated in various relevant scenarios at an automotive proving ground. Full article

The risk of serious crashes is notably higher among young and novice drivers. This increased risk is due to several factors, including a lack of recognition of dangerous situations, an overestimation of driving skills, and vulnerability to peer pressure. Recently, advanced driver assistance systems (ADAS) have been integrated into vehicles to help mitigate crashes linked to these factors. While numerous studies have examined ADAS broadly, few have specifically investigated its effects on young and novice drivers. This study aimed to address that gap by exploring ADAS’s impact on these drivers. Most studies in this review conclude that ADAS is beneficial for young and novice drivers, though some research suggests its impact may be limited or even negligible. Tailoring ADAS to address the unique needs of young drivers could enhance both the system’s acceptance and reliability. The review also found that unimodal warnings (e.g., auditory or visual) are as effective as multimodal warnings. Of the different types of warnings, auditory and visual signals proved the most effective. Additionally, ADAS can influence young drivers’ car-following behavior; for instance, drivers may maintain greater safety buffers or drive closely to avoid alarm triggers, likely due to perceived system unreliability. Aggressive drivers tend to benefit most from active ADAS, which actively intervenes to assist the driver. Future research could explore the combined effects of multiple ADAS functions within a single vehicle on young and novice drivers to better understand how these systems interact and impact driver behavior. Full article

This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative Braking Strategy (WARBS) system, which leverages onboard sensors and data processing capabilities to enhance the energy efficiency of regenerative braking across diverse weather conditions while minimizing unnecessary alerts. To achieve this, we develop driving style recognition models that integrate road conditions, such as weather and road friction, with different driving styles. Next, we propose an adaptive deceleration plan that aims to maximize the conversion of kinetic energy into electrical energy for the vehicle’s battery under varying weather conditions, considering vehicle dynamics and speed constraints. Given that the potential for energy recovery through regenerative braking is diminished on icy and snowy roads compared to dry ones, our approach introduces a driving context recognition system to facilitate effective speed planning. Both simulation and experimental validation indicate that this approach can significantly enhance overall energy efficiency. Full article

In modern automotive engineering, accurate vehicle sideslip angle estimation is crucial for enhancing vehicle safety, performance, and driver comfort. This paper addresses the challenge of estimating sideslip angle, an essential parameter for advanced driver-assistance systems (ADAS) and autonomous driving technologies. This study introduces a combined dynamic–kinematic extended Kalman filter (DK-EKF) approach that leverages the strengths of both kinematic and dynamic models while mitigating their individual limitations. The proposed DK-EKF enhances observability in low yaw rate conditions, a common issue with kinematic models, and improves the robustness of dynamic models against parameter uncertainties. A validation is conducted through extensive experimental tests, demonstrating the DK-EKF’s superior performance in various driving scenarios. The results confirm the efficacy of the proposed method in providing reliable sideslip angle estimation. Full article

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. Full article

The fusion of multiple sensors’ data in real-time is a crucial process for autonomous and assisted driving, where high-level controllers need classification of objects in the surroundings and estimation of relative positions. This paper presents an open-source framework to estimate the distance between a vehicle equipped with sensors and different road objects on its path using the fusion of data from cameras, radars, and LiDARs. The target application is an Advanced Driving Assistance System (ADAS) that benefits from the integration of the sensors’ attributes to plan the vehicle’s speed according to real-time road occupation and distance from obstacles. Based on geometrical projection, a low-level sensor fusion approach is proposed to map 3D point clouds into 2D camera images. The fusion information is used to estimate the distance of objects detected and labeled by a Yolov7 detector. The open-source pipeline implemented in ROS consists of a sensors’ calibration method, a Yolov7 detector, 3D point cloud downsampling and clustering, and finally a 3D-to-2D transformation between the reference frames. The goal of the pipeline is to perform data association and estimate the distance of the identified road objects. The accuracy and performance are evaluated in real-world urban scenarios with commercial hardware. The pipeline running on an embedded Nvidia Jetson AGX achieves good accuracy on object identification and distance estimation, running at 5 Hz. The proposed framework introduces a flexible and resource-efficient method for data association from common automotive sensors and proves to be a promising solution for enabling effective environment perception ability for assisted driving. Full article

This paper presents a novel approach to enhancing the safety of Advanced Driver Assistance Systems (ADAS) by integrating teleoperation for the remote control of ADAS features in a vehicle. The primary contribution of this research is the development and implementation of a teleoperation system that allows human operators to take control of the vehicle’s ADAS features, enabling timely intervention in critical situations where autonomous functions may be insufficient. While the concept of teleoperation has been explored in the literature, with several implementations focused on the direct control of vehicles, there are relatively few examples of teleoperation systems designed specifically to utilize ADAS features. This research addresses this gap by exploring teleoperation as a supplementary mechanism that allows human intervention in critical driving situations, particularly where autonomous systems may encounter limitations. The teleoperation system was tested under two critical ADAS scenarios, cruise control and lane change assist, chosen for their importance in real-world driving conditions. These scenarios demonstrate how teleoperation can complement and enhance the performance of ADAS features. The experiments reveal the effectiveness of remote control in providing precise control, allowing for swift and accurate responses in scenarios where the autonomous system might face challenges. The novelty of this work lies in its application of teleoperation to ADAS features, offering a new perspective on how human intervention can enhance vehicle safety. The findings provide valuable insights into optimizing teleoperation for real-world driving scenarios. As a result of the experiments, it was demonstrated that integrating teleoperation with ADAS features offers a more reliable solution compared to standalone ADAS driving. Full article

Alarm sounds significantly influence a user’s sensory perception while driving, directly affecting driving judgement and safety. Personal experience and the environment play an important role in information cognition, but they are rarely considered in the current warning design. We propose a methodology enabling engineers and designers to locally optimize the advanced driver-assistance system (ADAS) functions and applied it to the Shanghainese ecosystem to improve performance. The alarm sound content is studied and sorted out to conduct user research and spatial sound collection evaluation. Local optimization and the subdivision of data are carried out to generate a user perception set on which the experimental tests and evaluation analysis are implemented. The framework increases the overall efficiency of auditory warning systems and minimizes Human–Machine Interface misunderstandings, thus providing the optimal security scheme for users. Full article

Advanced Driver Assistance Systems (ADASs) can have a significant role in contributing to road safety. However, technological innovations are gaining user acceptance at different rates, which is a key factor for their widespread deployment. In Hungary, learning about ADASs is not part of the curriculum in driving schools. In the present research, a questionnaire survey was conducted among driving examiners to assess their level of ADAS awareness. The results show that, in general, examining officers have a positive attitude towards the technology and are quite knowledgeable about it, compared to drivers. However, education and information are also essential for them, as there are few places to obtain credible information. Full article

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. Full article

At present Deep Neural Networks (DNN) have a dominant role in the AI-driven Autonomous driving approaches. This paper focuses on the potential safety risks of deploying DNN classifiers in Advanced Driver Assistance System (ADAS) systems. In our experience, many theoretically sound AI-driven solutions tested and deployed in ADAS have shown serious safety flaws in practice. A brief review of practice and theory of automotive safety standards and related body of knowledge is presented. It is followed by a comparative analysis between DNN classifiers and safety standards developed in the automotive industry. The output of the study provides advice and recommendations for filling the current gaps within the complex and interrelated factors pertaining to the safety of Autonomous Road Vehicles (ARV). This study may assist ARV’s safety, system, and technology providers during the design, development, and implementation life cycle. The contribution of this work is to highlight and link the learning rules enforced by risk factors when DNN classifiers are expected to provide a near real-time safer Vehicle Navigation Solution (VNS). Full article