Search Results (102)

Occupational therapists need objective tools to evaluate and provide interventions that promote the recovery and rehabilitation of clients. Driving, a common goal for clients after an injury or illness, is a complex task that relies on visual, cognitive, and motor skills. The Functional Object Detection and Functional Object Detection (FOD)—Advanced driving simulator scenarios were developed to provide objective and repeatable driving experiences allowing clinicians to assess their clients’ forward (focal) and peripheral vision, lane keeping, and speed maintenance, as well as provide interventions. Using FOD—Advanced, clinicians can adjust variables to create various task scenarios or combinations to meet the client’s needs and facilitate recovery by providing an appropriate challenge. This study examined four driving simulator scenario combinations and age-related differences for one combination. Study 1 explored older adults’ performance using four possible combinations of FOD—Advanced. Five out of eleven variables (average target reaction time, percentage of targets detected, average brake reaction time, number of target extra presses, and average speed) were effective in distinguishing among the four combinations of FOD with a cross-validated classification rate of 72%. In Study 2, one combination was selected from Study 1 and a group of teens completed the same tasks to evaluate age-related differences. Four out of thirteen simulator variables (standard deviation of brake reaction time, number of target extra presses, average target reaction time, and standard deviation of target reaction time) maximally distinguished the older adults from the younger participants with a cross-validated classification accuracy of 78%. Implications and recommendations for clinical practice and future research are provided. Full article

This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track detector utilizes YOLOv5, trained on custom datasets generated from public videos captured on snowy roads. Video frames are annotated with the Computer Vision Annotation Tool (CVAT) to identify pixels containing snow tire tracks. To mitigate overfitting, the detector is trained on a combined dataset that incorporates both snow tire track images and road scenes from the Udacity dataset. The lane center estimator uses the detected tire tracks to estimate a reference line for lane keeping. Detected tracks are binarized and transformed into a bird’s-eye view image. Then, skeletonization and Hough transformation techniques are applied to extract tire track lines from the classified pixels. Finally, the Kalman filter estimates the lane center based on tire track lines. Evaluations conducted on unseen images demonstrate that the proposed algorithm provides a reliable lane reference, even under heavy snowfall conditions. Full article

This paper focuses on the acceptance of ADASs in the traffic safety and human factor domain. More specifically, it examines the predictive validity of the Unified Model of Driver Acceptance (UMDA) for an ADAS bundle that includes forward collision warning, headway monitoring and warning, and lane-keeping assistance in Belgium and Vietnam, two substantially different geographical, socio-cultural, and macroeconomic settings. All systems in the studied ADAS bundle are located at the Society of Automotive Engineer (SAE)-level 0 of automation. We found moderate acceptance towards such an ADAS bundle in both countries, and respondents held rather positive opinions about system-specific characteristics. In terms of predictive validity, the UMDA scored quite well in both countries, though better in Belgium than in Vietnam. Macroeconomic factors and socio-cultural characteristics could explain these differences between the two countries. Policymakers are encouraged to prioritise initiatives that stimulate the purchase and use of the ADAS, rather than on measures meant to influence the underlying decisional balance. Full article

Among the many technological challenges of automated driving development, there is an increasing focus on the behavior of these systems. Behavior is usually associated with multiple layers of control. In this paper, we focus on motion planning and control, and how these layers can be tailored to produce different behavior. Our review aims to collect and judge the most used techniques in the field of path planning and control. It has been revealed that model predictive planning and control provides high flexibility, with the cost of high computational capacity. There are simpler algorithms, such as pure-pursuit and Stanley controllers, however, these have very few parameters, therefore, the number of possible behavior patterns is limited. Full article

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. Full article

Automated driving is a promising development in reducing driving accidents and improving the efficiency of driving. This study focuses on developing a decision-making strategy for autonomous vehicles, specifically addressing maneuvers such as lane change, double lane change, and lane keeping on highways, using deep reinforcement learning (DRL). To achieve this, a highway driving environment in the commercial multi-body simulation software IPG Carmaker 11 version is established, wherein the ego vehicle navigates through surrounding vehicles safely and efficiently. A hierarchical control framework is introduced to manage these vehicles, with upper-level control handling driving decisions. The DDPG (deep deterministic policy gradient) algorithm, a specific DRL method, is employed to formulate the highway decision-making strategy, simulated in MATLAB software. Also, the computational procedures of both DDPG and deep Q-network algorithms are outlined and compared. A set of simulation tests is carried out to evaluate the effectiveness of the suggested decision-making policy. The research underscores the advantages of the proposed framework concerning its convergence rate and control performance. The results demonstrate that the DDPG-based overtaking strategy enables efficient and safe completion of highway driving tasks. Full article

In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, a diverse set of rewarding strategies yields a spectrum of realizable policies. Nevertheless, the challenge lies in discerning the optimal behavior that maximizes performance. Traditional approaches entail exhaustive training through a trial-and-error strategy across conceivable reward functions, which is a process notorious for its time-consuming nature and substantial financial implications. Contrary to conventional methodologies, the Monte Carlo Tree Search (MCTS) enables the prediction of reward function quality through Monte Carlo simulations, thereby eliminating the need for exhaustive training on all available reward functions. The findings obtained from MCTS simulations can be effectively leveraged to selectively train only the most suitable RL models. This approach helps alleviate the resource-heavy nature of traditional RL processes through altering the training pipeline. This paper validates the theoretical framework concerning the unique property of the Monte Carlo Tree Search algorithm by emphasizing its generality through highlighting crossalgorithmic and crossenvironmental capabilities while also showcasing its potential to reduce training costs. Full article

The study of lateral steering control for Automated Driving Systems identifies new control solutions more often than new control problems. This is likely due to the maturity of the field. To prevent repeating efforts toward solving already-solved problems, what is needed is a cohesive way of evaluating all developed controllers under a wide variety of environmental conditions. This work serves as a step in this direction. Four controllers are tested on five maneuvers representing highways and collision avoidance trajectories. Each controller and maneuver combination is repeated on five sets of environmental conditions or Operational Design Domains (ODDs). The design of these ODDs ensures the translation of these experimental results to real-world applications. The commercial software, CarSim 2020, is extended with Simulink models of the environment, sensor dynamics, and state estimation performances to perform highly repeatable and realistic evaluations of each controller. The results of this work demonstrate that most of the combinations of maneuvers and ODDs have existing cheap controllers that achieve satisfactorily safe performance. Therefore, this field’s research efforts should be directed toward finding new control problems in lateral path tracking rather than proposing new controllers for ODDs that are already solved. Full article

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance. Full article

To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver’s preference is consistent with this driver’s naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers’ real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers’ naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers’ real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers. Full article

As autonomous vehicles (AVs) are advancing to higher levels of autonomy and performance, the associated technologies are becoming increasingly diverse. Lane-keeping systems (LKS), corresponding to a key functionality of AVs, considerably enhance driver convenience. With drivers increasingly relying on autonomous driving technologies, the importance of safety features, such as fail-safe mechanisms in the event of sensor failures, has gained prominence. Therefore, this paper proposes a reinforcement learning (RL) control method for lane-keeping, which uses surrounding object information derived through LiDAR sensors instead of camera sensors for LKS. This approach uses surrounding vehicle and object information as observations for the RL framework to maintain the vehicle’s current lane. The learning environment is established by integrating simulation tools, such as IPG CarMaker, which incorporates vehicle dynamics, and MATLAB Simulink for data analysis and RL model creation. To further validate the applicability of the LiDAR sensor data in real-world settings, Gaussian noise is introduced in the virtual simulation environment to mimic sensor noise in actual operational conditions. Full article

The operation of the automatic emergency braking (AEB) system may lead to a significant increase in lateral offset of vehicles in curved road conditions, which can pose a potential risk of collisions with vehicles in adjacent lanes or road edges. In order to address this issue, this study proposes an integrated longitudinal and lateral control strategy for collision avoidance during emergency braking, which utilizes a control algorithm based on Time to Collision (TTC) for longitudinal control and a control algorithm based on yaw angle and preview point lateral deviation for lateral control. On one hand, the AEB system facilitates proactive longitudinal intervention to prevent collisions in the forward direction. On the other hand, the Lane Keeping Assist (LKA) system allows for lateral intervention, reducing the lateral offset of the vehicle during braking. To evaluate the effectiveness of this integrated control strategy, a collaborative simulation model involving Matlab/Simulink, PreScan, and CarSim is constructed. Under typical curved road conditions, comparative simulations are conducted among three different control systems: ➀ AEB control system alone; ➁ independent control system of AEB and LKA; and ➂ integrated control system of AEB and LKA. The results indicate that although all three control systems are effective in preventing longitudinal rear-end collisions, the integrated control system outperforms the other two control systems significantly in suppressing the vehicle’s lateral offset. In the scenario with a curve radius of 60 m and an initial vehicle speed of 60 km/h, System ➀ exhibits a lateral offset from the lane centerline reaching up to 1.72 m. In contrast, Systems ➁ and ➂ demonstrate significant improvements with lateral offsets of 0.29 m and 0.21 m, respectively. Full article

Lane detection is a vital component of intelligent driving systems, offering indispensable functionality to keep the vehicle within its designated lane, thereby reducing the risk of lane departure. However, the complexity of the traffic environment, coupled with the rapid movement of vehicles, creates many challenges for detection tasks. Current lane detection methods suffer from issues such as low feature extraction capability, poor real-time detection, and inadequate robustness. Addressing these issues, this paper proposes a lane detection algorithm that combines an online re-parameterization ResNet with a hybrid attention mechanism. Firstly, we replaced standard convolution with online re-parameterization convolution, simplifying the convolutional operations during the inference phase and subsequently reducing the detection time. In an effort to enhance the performance of the model, a hybrid attention module is incorporated to enhance the ability to focus on elongated targets. Finally, a row anchor lane detection method is introduced to analyze the existence and location of lane lines row by row in the image and output the predicted lane positions. The experimental outcomes illustrate that the model achieves F1 scores of 96.84% and 75.60% on the publicly available TuSimple and CULane lane datasets, respectively. Moreover, the inference speed reaches a notable 304 frames per second (FPS). The overall performance outperforms other detection models and fulfills the requirements of real-time responsiveness and robustness for lane detection tasks. Full article

Radars in the W-band are being integrated into car bumpers for functionalities such as adaptive cruise control, collision avoidance, or lane-keeping. These Advanced Driving Assistance Systems (ADAS) enhance traffic security in coordination with Intelligent Transport Systems (ITS). This paper analyzes the attenuation effect that car bumpers cause on the signals passing through them. Using the free-space transmission technique inside an anechoic chamber, we measured the attenuation caused by car bumper samples with different material compositions. The results show level drops lower than 1.25 dB in all the samples analyzed. The signal attenuation triggered by the bumpers decreases with the frequency, with differences ranging from 0.55 dB to 0.86 dB when comparing the end frequencies within the radar band. Among the analyzed bumper samples, those with a thicker varnish layer or with talc in the composition seem to attenuate more. We also provide an estimation of the measurement uncertainty for the validation of the obtained results. Uncertainty analysis yields values below 0.21 dB with a 95% coverage interval in the measured frequency band. When comparing the measured value with its uncertainty, i.e., the relative uncertainty, the lower the frequency in the measured band, the more accurate the measurements seem to be. Full article

In the rapidly advancing domain of vehicular traffic management and autonomous driving, accurate lane change predictions are paramount for ensuring safety and optimizing traffic flow. This study introduces a comprehensive two-stage prediction model that harnesses the capabilities of long short-term memory (LSTM) for anticipating vehicular lane changes. Initially, we employed a variety of models, such as regression methods, SVMs, and a multilayer perceptron, to categorize lane change behaviors. The dataset was then segmented based on vehicle trajectories and lane change patterns. In the subsequent phase, we utilized the superior classification outcomes from LinearSVC to curate our training data. We developed two dedicated LSTM networks tailored to specific datasets: the lane-keeping LSTM (LK-LSTM) and the lane-changing LSTM (LC-LSTM). By integrating insights from both models, we achieved a comprehensive prediction of vehicular lane changes. Our results indicate that the unified prediction model markedly enhances prediction precision. Accurate lane change predictions offer valuable contributions to advanced driver-assistance systems (ADAS), with the potential to minimize traffic mishaps and enhance traffic fluidity. As we transition to a more autonomous automotive era, refining these predictions becomes essential in seamlessly merging human and automated driving experiences. Full article