Autonomous vehicles require safe motion planning in uncertain environments, which are largely caused by surrounding vehicles. In this paper, a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover. First, a 4-degree of freedom vehicle dynamics model, and a rollover risk index are introduced. Besides, the uncertainty of surrounding vehicles' position is processed and propagated based on the Extended Kalman Filter method. Then, the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles. In addition, the model predictive controller is designed as the motion planning framework which accounts for the rollover risk, the position uncertainty of the surrounding vehicles, and vehicle dynamic constraints of autonomous vehicles. Furthermore, two edge cases, the cut-in scenario, and merging scenario are designed. Finally, the safety, effectiveness, and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.

Driving comfort is a crucial consideration in the automotive industry. In the realm of autonomous driving, comfort has always been a factor that requires continuous improvement. A common approach to improving driving comfort is through the optimization of local path planning. Nevertheless, it is imperative to recognize that macroscopic factors, including traffic flow and road conditions, wield a substantial influence on comfort. For instance, complex traffic scenarios increase the possibility of emergency braking, thereby affecting comfort. Consequently, investigating the intricate interplay between comfort and global path planning becomes essential. This paper introduces a methodology and framework for predicting driving comfort by leveraging road information. The study established a road information-driving comfort dataset and devised prediction models using multi-head attention mechanism. The ensuing discussion elucidates the practical application of the model in path planning through examples and tests. Following the path optimized by the model, the vehicles exhibited a reduction in jerk. This research predicted driving comfort based on road information and integrated it with global path planning, which holds significant implications for autonomous driving navigation systems and provides a valuable reference for related research.

Compliance with traffic laws is a fundamental requirement for human drivers on the road, and autonomous vehicles must adhere to traffic laws as well. However, current autonomous vehicles prioritize safety and collision avoidance primarily in their decision-making and planning, which will lead to misunderstandings and distrust from human drivers and may even result in accidents in mixed traffic flow. Therefore, ensuring the compliance of the autonomous driving decision-making system is essential for ensuring the safety of autonomous driving and promoting the widespread adoption of autonomous driving technology. To this end, the paper proposes a trigger-based layered compliance decision-making framework. This framework utilizes the decision intent at the highest level as a signal to activate an online violation monitor that identifies the type of violation committed by the vehicle. Then, a four-layer architecture for compliance decision-making is employed to generate compliant trajectories. Using this system, autonomous vehicles can detect and correct potential violations in real-time, thereby enhancing safety and building public confidence in autonomous driving technology. Finally, the proposed method is evaluated on the DJI AD4CHE highway dataset under four typical highway scenarios: speed limit, following distance, overtaking, and lane-changing. The results indicate that the proposed method increases the vehicle's overall compliance rate from 13.85% to 84.46%, reduces the proportion of active violations to 0%, and only slightly decreases the vehicle's traffic efficiency by 2.7%, demonstrating its effectiveness.

Suspension control systems are in need for more information of road roughness conditions to improve their performance under different roads. Existing methods of gauging road roughness are limited, and they usually involve visual inspections or special vehicles equipped with instruments that can gauge physical measurements of road irregularities. This paper proposes data collection for a period of a time from accelerometers fixed on unsprung mass and uses the mean square values of this datasets divided by vehicle speed to classify the roughness conditions of a section of a road. This approach is possible due to the existence of relationships between the power spectral densities of the road surface, unsprung mass accelerations via a transfer function, and vehicle speed. This paper gave the relationship between the resolution of road roughness classification and the length of time‐window and suggestions about choosing the appropriate time‐window length on the balance of road roughness resolution and classification delay. Moreover, to enhance the stability of classification, the influence of damping parameters of vehicle suspension on the classification output is studied, and a classification method of road roughness is proposed based on neural network and damping coefficient correction.

Safety is a long-standing and final pursuit in the development of autonomous driving systems, with many safety challenge arising from perception. How to effectively evaluate the safety and reliability of perception algorithms is becoming an emerging issue. Despite its critical importance, existing perception methods exhibit a limitation in their robustness, primarily due to benchmarks being entirely simulated, which fail to align predicted results with actual outcomes, particularly under extreme weather conditions and sensor anomalies that are prevalent in real-world scenarios. To fill this gap, in this study, we propose a Sim-to-Real Evaluation Benchmark for autonomous driving (S2R-Bench). We collect diverse sensor anomaly data across various road conditions to evaluate the robustness of perception methods comprehensive and realistic manner. This is the first corruption robustness dataset based on real-world scenarios, encompassing various road conditions, weather conditions, lighting intensities, and time periods. By comparing real-world data with simulated data, we demonstrate the reliability the collected data for real-world use and aim to foster research on more robust perception models for autonomous driving.

In order to investigate the effects of different shapes of hydrogen inlet ports on the behavioral characteristics of hydrogen in Type IV hydrogen storage cylinders under rapid refueling conditions, a mathematical model of hydrogen temperature rise and a three-dimensional numerical analysis model were developed. The rectangular, hexagonal, triangular, Reuleaux triangular, circular, elliptical and conical inlet ports were researched by using computational fluid dynamics methods. The results showed that, for the same refueling flow rate and cross-sectional area, the hydrogen temperature inside a cylinder with a rectangular inlet port is higher and the jet tilt angle is larger than for a hexagonal port, while the thermal stratification phenomenon is not obvious. The hydrogen temperature inside a cylinder with a triangular inlet port is lower than that with a Reuleaux triangle port and the jet tilt angle is larger, and neither has significant thermal stratification. The hydrogen temperature inside a cylinder with a circular inlet port is higher than that with an ellipse port, the jets are not tilted on either one, and the phenomenon of thermal stratification is prominent. Further analysis indicated that enlarging the cross-sectional area and increasing the refueling flow rate results in a higher hydrogen temperature and intensified thermal stratification and an upward-angled jet can effectively reduce or eliminate thermal stratification.