Search Results (97)

Light detection and ranging (LiDAR) provides three-dimensional environmental information that is critical for maintaining the safety and reliability of autonomous driving systems. However, dust accumulation on the LiDAR window can cause detection errors and degrade performance. This study determined the optimal spray conditions for accumulating dust to evaluate LiDAR sensor cleaning performance. A primary optimization experiment using spray pressure, spray speed, spray distance, and the number of sprays as variables showed that spray pressure and number of sprays had the most significant influence on the kinetic energy and distribution of dust particles. Notably, the interaction between spray distance and number of sprays—related to curvature effects—was identified as a key variable increasing process sensitivity. A supplementary experiment, which added spray angle as a variable, indicated that while spray pressure remained the most significant factor, spray angle and number of sprays had an indirect influence through interaction terms. Both experiments used the same response variable (point cloud data) interactions to stepwise analyze particle transfer and spatial diffusion. The resulting optimal conditions offer a standard basis for evaluating LiDAR cleaning performance and may help improve cleaning efficiency and maintenance strategies. Full article

In modern vehicles, Autonomous Driving Systems (ADSs) are designed to operate partially or fully without human intervention. The ADS pipeline comprises multiple layers, including sensors, perception, localization, mapping, path planning, and control. The Robot Operating System (ROS) is a widely adopted framework that supports the modular development and integration of these layers. Among them, the path-planning and control layers remain particularly challenging due to several limitations. Classical path planners often struggle with non-smooth trajectories and high computational demands. Meta-heuristic optimization algorithms have demonstrated strong theoretical potential in path planning; however, they are rarely implemented in real-time ROS-based systems due to integration challenges. Similarly, traditional PID controllers require manual tuning and are unable to adapt to system disturbances. This paper proposes a ROS-based ADS architecture composed of eight integrated nodes, designed to address these limitations. The path-planning node leverages a meta-heuristic optimization framework with a cost function that evaluates path feasibility using occupancy grids from the Hector SLAM and obstacle clusters detected through the DBSCAN algorithm. A dynamic goal-allocation strategy is introduced based on the LiDAR range and spatial boundaries to enhance planning flexibility. In the control layer, a modified Pure Pursuit algorithm is employed to translate target positions into velocity commands based on the drift angle. Additionally, an adaptive PID controller is tuned in real time using the Differential Evolution (DE) algorithm, ensuring robust speed regulation in the presence of external disturbances. The proposed system is practically validated on a four-wheel differential drive robot across six scenarios. Experimental results demonstrate that the proposed planner significantly outperforms state-of-the-art methods, ranking first in the Friedman test with a significance level less than 0.05, confirming the effectiveness of the proposed architecture. Full article

With the development of electrification and intelligent technologies in vehicles, ride comfort issues represented by motion sickness have become a key constraint on the performance of autonomous driving. The occurrence of motion sickness is influenced by the comprehensive movement of the vehicle in the longitudinal, lateral, and vertical directions, involving ACC, LKA, active suspension, etc. Existing motion sickness control method focuses on optimizing the longitudinal, lateral, and vertical directions separately, or coordinating the optimization control of the longitudinal and lateral directions, while there is relatively little research on the coupling effect and coupled optimization of the longitudinal and vertical directions. This study proposes a coupled framework of ACC and active suspension control system based on MPC. By adding pitch angle changes caused by longitudinal acceleration to the suspension model, a coupled state equation of half-car vertical dynamics and ACC longitudinal dynamics is constructed to achieve integrated optimization of ACC and suspension for motion suppression. The suspension active forces and vehicle acceleration are regulated coordinately to optimize vehicle vertical, longitudinal, and pitch dynamics simultaneously. Simulation experiments show that compared to decoupled control of ACC and suspension, the integrated control framework can be more effective. The research results confirm that the dynamic coordination between the suspension and ACC system can effectively suppress the motion sickness, providing a new idea for solving the comfort conflict in the human vehicle environment coupling system. Full article

Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article

With continuous advancements in autonomous driving technology, systematic and reliable safety verification is becoming increasingly important. However, despite the active development of various X-in-the-loop simulation (XILS) platforms to validate autonomous driving systems (ADSs), standardized evaluation frameworks for assessing the credibility of the simulation platforms themselves remain lacking. Therefore, we propose a novel integrated credibility-assessment methodology that combines dynamics-based fidelity assessment, parameter-based reliability assessment, and scenario-based reliability assessment. These three techniques evaluate the similarity and consistency between XILS and real-world test data based on statistical and mathematical comparisons. The three consistency measures are then utilized to derive a dynamics-based correlation metric for fidelity, along with parameter-based and scenario-based correlation and applicability metrics for reliability. The novel contribution of this paper lies in a geometric similarity analysis methodology that significantly enhances the efficiency of credibility assessment. We propose a methodology that enables geometric similarity assessment through spider chart visualization of metrics derived from the credibility-assessment process and shape comparison, based on Procrustes, Fréchet, and Hausdorff distances. As a result, speed is not a dominant factor for credibility evaluation, enabling assessment with a single representative speed test; the framework simplifies the XILS evaluation and enhances ADS validation efficiency. Full article

Accurate traffic light status detection and the appropriate response to changes in that status are crucial for autonomous driving systems (ADSs) starting from SAE Level 3 automation. The dilemma zone problem occurs during the amber phase of traffic lights, when the ADS must decide whether to stop or proceed through the intersection. This paper proposes a methodology for developing a runtime monitor that addresses the dilemma zone problem and monitors the autonomous vehicle’s behavior at traffic lights, ensuring that the ADS’s decisions align with the system’s safety requirements. This methodology yields a set of safety requirements formulated in controlled natural language, their formal specification in linear temporal logic (LTL), and the implementation of a corresponding runtime monitor. The monitor is integrated within a safety-oriented software architecture through a modular autonomous driving system pipeline, enabling real-time supervision of the ADS’s decision-making at intersections. The results show that the monitor maintained stable and fast reaction times between 40 ms and 65 ms across varying speeds (up to 13 m/s), remaining well below the 100 ms threshold required for safe autonomous operation. At speeds of 30, 50, and 70 km/h, the system ensured correct behavior with no violations of traffic light regulations. Furthermore, the monitor achieved 100% detection accuracy of the relevant traffic lights within 76 m, with high spatial precision (±0.4 m deviation). While the system performed reliably under typical conditions, it showed limitations in disambiguating adjacent, irrelevant signals at distances below 25 m, indicating opportunities for improvement in dense urban environments. Full article

Rapid progress in lithium-ion batteries and AI-powered autonomous driving is poised to propel electric vehicles to a 50% share of the global automotive market by the year 2035. Today, there is a major focus on recycling electric vehicle motors, particularly on extracting rare earth elements (REEs) from NdFeB permanent magnets (PMs). This research is based on a single-furnace process concept designed to separate metal components within PM motors by exploiting the varying melting points of the constituent materials, simultaneously extracting REEs present within the PMs and transferring them into the slag phase. Thermodynamic modeling, via Factsage Equilib stream calculations, optimized the experimental process. Simulated materials substituted the PM motor, which optimized modeling-directed melting within an induction furnace. The 2FeO·SiO₂ fayalite flux can oxidize rare earth elements, resulting in slag. The neodymium oxidation reaction by fayalite exhibits a ΔG° of −427 kJ when subjected to an oxygen partial pressure ($P_{O_2}$) of 1.8 × 10⁻⁹, which is lower than that required for FeO decomposition. Concerning the FeO–SiO₂ system, neodymium, in Nd³⁺, exhibits a strong bonding with the ${\mathrm{S}\mathrm{i}\mathrm{O}}_4^{4^{-}}$ matrix, leading to its incorporation within the slag as the silicate compound, Nd₂Si₂O₇. When 30 wt.% fayalite flux was added, the resulting experiment yielded a neodymium extraction degree of 91%, showcasing the effectiveness of this fluxing agent in the extraction process. Full article

With the rapid advancement of autonomous driving (AD) technology, its application in road traffic has garnered increasing attention. This study analyzes 534 AD and 82,030 human driver traffic accidents and employs SMOTE to balance the sample sizes between the two groups. Using association rule mining, this study identifies key risk factors and behavioral patterns. The results indicate that while both AD and human driver accidents exhibit seasonal trends, their risk characteristics and distributions differ markedly. AD accidents are more frequent in summer (July–August) on clear days and tend to occur at intersections and on streets, with a higher proportion of non-injury collisions observed at night. Collisions involving non-motorized road users are more prevalent in human driver accidents. AD systems show certain advantages in detecting non-motorized vehicles and performing low-speed evasive maneuvers, particularly at night; however, limitations remain in perception and decision-making under complex conditions. Human driver accidents are more susceptible to driver-related factors such as fatigue, distraction, and risk-prone behaviors. Although AD accidents generally result in lower injury severity, further technological refinement and scenario adaptability are required. This study provides insights and recommendations to enhance the safety performance of both AD and human-driven systems, offering valuable guidance for policymakers and developers. Full article

Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS platforms, evaluating their functionalities, strengths, and limitations. Through an extensive literature review, the survey explores their adoption and utilization across key research domains. Additionally, it identifies emerging trends shaping the field. The main contributions of this survey include (1) a detailed overview of leading open-source platforms, highlighting their strengths and weaknesses; (2) an examination of their impact on research; and (3) a synthesis of current trends, particularly in interoperability with emerging technologies such as AI/ML solutions and edge computing. This study aims to provide researchers and practitioners with a holistic understanding of open-source ADS platforms, guiding them in selecting the right platforms for future innovation. Full article

This study explores the trends and causes of disengagement events in Autonomous Vehicles (AVs) using data from the California Department of Motor Vehicles (CA DMV) from 2019 to 2022. Disengagements, defined as instances where control transitions from the AV to a human driver, are crucial indicators of the reliability and trustworthiness of Autonomous Driving Systems (ADS). The analysis identifies a significant correlation between cumulative mileage and disengagement frequency, revealing that 77% of disengagements were initiated by safety drivers. The research categorizes disengagements into system-initiated, driver-initiated, or planned for testing purposes, highlighting that environmental factors and interactions with other road users are the primary causes attributed to the AV system. The findings indicate a downward trend in the ratio of disengagements to mileage, suggesting improvements in AV technology and increasing operator trust. However, the persistent rate of manual disengagements underscores ongoing challenges regarding driver confidence. This research enhances the understanding of ADS performance and driver interactions, offering valuable insights for improving AV safety and fostering technology acceptance in mixed-traffic environments. Future studies should prioritize enhancing system reliability and addressing the psychological factors that influence driver trust in ADS. Full article

Trajectory prediction plays a crucial role in level autonomous driving systems, as real-time and accurate trajectory predictions can significantly enhance the safety of autonomous vehicles and the robustness of the autonomous driving system. We propose a novel trajectory prediction model that adopts the encoder–decoder paradigm. In the encoder, we introduce a dual-thread interaction relationship encoding method based on a sparse graph attention mechanism, which allows our model to aggregate richer multimodal interaction information. Additionally, we introduce a non-autoregressive query generation method that reduces the model’s inference time by approximately 80% through the parallel generation of decoding queries. Finally, we propose a multi-stage decoder that generates more accurate and reasonable predicted trajectories by predicting trajectory reference points and performing spatial and posture optimization on the predicted trajectories. Comparative experiments with existing advanced algorithms demonstrate that our method improves the minimum Average Displacement Error (minADE), minimum Final Displacement Error (minFDE), and Miss Rate (MR) by 10.3%, 10.3%, and 14.5%, respectively, compared to the average performance. Lastly, we validate the effectiveness of the various modules proposed in this paper through ablation studies. Full article

Diversified and realistic traffic scenarios are a crucial foundation for evaluating the safety of autonomous driving systems in simulations. However, a considerable number of current methods generate scenarios that lack sufficient realism. To address this issue, this paper proposes a vehicle trajectory generation method based on vehicle–vehicle and vehicle–map interaction pattern learning. By leveraging a multihead self-attention mechanism, the model efficiently captures complex dependencies among vehicles, enhancing its ability to learn realistic traffic dynamics. Moreover, the multihead cross-attention mechanism is also used to learn the interaction features between the vehicles and the map, addressing the challenge of trajectory generation’s difficulty in perceiving static environments. This proposed method enhances the model’s ability to learn natural traffic sequences, enable the generation of more realistic traffic flow, and provide strong support for the testing and optimization of autonomous driving systems. Experimental results show that compared to the Trafficgen baseline model, the proposed method achieves a 26% improvement in ADE and a 20% improvement in FDE. Full article

Autonomous driving (AD) significantly reduces road accidents, providing safer transportation while optimizing traffic flow for greater efficiency and smoothness. However, ensuring safe decision-making in dynamic and complex highway environments, especially during lane-changing maneuvers, remains a challenge. Reinforcement Learning (RL) has become a promising method for developing decision-making systems in AD, particularly Deep Reinforcement Learning (DRL). In this study, we focus on highway lane-change behaviors and propose a novel DRL algorithm, called Huber-regularized Reward-threshold Adaptive Double Deep Q-Network (HRA-DDQN). First, a reward function optimally balances speed, safety, and the necessity of lane changes, ensuring efficient and safe maneuvering in highway scenarios. Second, the dynamic target network update strategy triggered by reward difference is introduced into HRA-DDQN, which enhances the model’s adaptability to varying traffic conditions. Finally, a hybrid loss function, combining Huber loss with L2 regularization, is implemented in HRA-DDQN to improve robustness against outliers and mitigate overfitting. Simulation results demonstrate that the proposed decision framework significantly enhances both driving efficiency and safety, outperforming other methods by yielding higher rewards, lower collision rates, and more stable lane-changing decisions. Full article

LiDAR and camera systems are frequently used together to gain a more complete understanding of the environment in different fields, such as mobile robotics, autonomous driving, or intelligent surveillance. Accurately calibrating the extrinsic parameters is crucial for the accurate fusion of the data captured by both systems, which is equivalent to finding the transformation between the reference systems of both sensors. Traditional calibration methods for LiDAR and camera systems are developed for pinhole cameras and are not directly applicable to fisheye cameras. This work proposes a target-based calibration method for LiDAR and fisheye camera systems that avoids the need to transform images to a pinhole camera model, reducing the computation time. Instead, the method uses the spherical projection of the image, obtained with the intrinsic calibration parameters and the corresponding point cloud for LiDAR–fisheye calibration. Thus, unlike a pinhole-camera-based system, a wider field of view is provided, adding more information, which will lead to a better understanding of the environment itself, as well as enabling using fewer image sensors to cover a wider area. Full article

Autonomous driving (AD) technology has seen significant advancements in recent years; however, challenges remain, particularly in achieving reliable performance under adverse weather conditions such as heavy fog. In response, we propose a multi-class fog density classification approach to enhance the AD system performance. By categorizing fog density into multiple levels (25%, 50%, 75%, and 100%) and generating separate datasets for each class using the CARLA simulator, we improve the perception accuracy for each specific fog density level and analyze the effects of varying fog intensities. This targeted approach offers benefits such as improved object detection, specialized training for each fog class, and increased generalizability. Our results demonstrate enhanced perception of various objects, including cars, buses, trucks, vans, pedestrians, and traffic lights, across all fog densities. This multi-class fog density method is a promising advancement toward achieving reliable AD performance in challenging weather, improving both the precision and recall of object detection algorithms under diverse fog conditions. Full article