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Brake judder affects vehicle safety and comfort, making it a key area of research in brake NVH. Transfer path analysis is effective for analyzing and reducing brake judder. However, current studies mainly focus on passenger cars, with limited investigation into commercial vehicles. The complex chassis structures of commercial vehicles involve multiple transfer paths, resulting in extensive data and testing challenges. This hinders the analysis and suppression of brake judder using transfer path analysis. In this study, we propose a simulation-based method to investigate brake judder transfer paths in commercial vehicles. Firstly, road tests were conducted to investigate the brake judder of commercial vehicles. Time-domain analysis, order characteristics analysis, and transfer function analysis between components were performed.

Lane change obstacle avoidance is a common driving scenario for autonomous vehicles. However, existing methods for lane change obstacle avoidance in vehicles decouple path and velocity planning, neglecting the coupling relationship between the path and velocity. Additionally, these methods often do not sufficiently consider the lane change behaviors characteristic of human drivers. In response to these challenges, this paper innovatively applies the Dynamic Movement Primitives (DMPs) algorithm to vehicle trajectory planning and proposes a real-time trajectory planning method that integrates DMPs and Artificial Potential Fields (APFs) algorithm (DMP-Fs) for lane change obstacle avoidance, enabling rapid coordinated planning of both path and velocity. The DMPs algorithm is based on the lane change trajectories of human drivers. Therefore, this paper first collected lane change trajectory samples from on-road vehicle experiments.

Multiple actuators equipped in electric vehicles, such as four- wheel steering (4WS) and four-wheel drive (4WD), provide more degrees of freedom for chassis motion control. However, developing independent control strategies for distinct actuator types could result in control conflicts, potentially degrading the vehicle's motion performance. To address this issue, a model predictive control (MPC) based steering-drive cooperated control strategy for enhanced agility and stability of electric vehicles with 4WD and 4WS is proposed in this paper. By designing the control constraints within the MPC framework, the strategy enables single-drive control, single-steering control, and steering-drive cooperative control. In the upper control layer, a linear time-varying MPC (LTV-MPC) is designed to generate optimal additional yaw moment and additional steering angles of front and rear wheels to enhance vehicle agility and lateral stability.

This paper focuses on lane-changing trajectory planning and trajectory tracking control in autonomous vehicle technology. Aiming at the lane-changing behavior of autonomous vehicles, this paper proposes a new lane-changing trajectory planning method based on particle swarm optimization (PSO) improved third-order Bezier curve path planning and polynomial curve speed planning. The position of Bezier curve control points is optimized by the particle swarm optimization algorithm, and the lane-changing trajectory is optimized to improve the comfort of lane changing process. Under the constraints of no-collision and vehicle dynamics, the proposed method can ensure that the optimal lane-changing trajectory can be found in different lane-changing scenarios. To verify the feasibility of the above planning algorithm, this paper designs the lateral and longitudinal controllers for trajectory tracking control based on the vehicle dynamic tracking error model.

The driver monitoring system (DMS) plays an essential role in reducing traffic accidents caused by human errors due to driver distraction and fatigue. The vision-based DMS has been the most widely used because of its advantages of non-contact and high recognition accuracy. However, the traditional RGB camera-based DMS has poor recognition accuracy under complex lighting conditions, while the IR-based DMS has a high cost. In order to improve the recognition accuracy of conventional RGB camera-based DMS under complicated illumination conditions, this paper proposes a lightweight low-illumination image enhancement network inspired by the Retinex theory. The lightweight aspect of the network structure is realized by introducing a pixel-wise adjustment function. In addition, the optimization bottleneck problem is solved by introducing the shortcut mechanism.

Harmony-with-traffic refers to the ability of autonomous vehicles to maximize the driving benefits such as comfort, efficiency, and energy consumption of themselves and the surrounding traffic during interactive driving under traffic rules. In the test of harmony-with-traffic, one or more background vehicles that can respond to the driving behavior of the vehicle under test are required. For this purpose, the functional requirements of car-following model for harmony-with-traffic evaluation are analyzed from the dimensions of test conditions, constraints, steady state and dynamic response. Based on them, an interactive car-following model (ICFM) is developed. In this model, the concept of equivalent distance is proposed to transfer lateral influence to longitudinal. The calculation methods of expected speed are designed according to the different car-following modes divided by interaction object, reaction distance and equivalent distance.

The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning.

In order to realize driving safety in highway scenarios, autonomous vehicles need to predict and reason about the driving intentions and motion trajectories of surrounding target vehicles in the near feature. Essentially, trajectory prediction of target vehicles can be viewed as a typical time series generation problem, which predicts the future trajectory of the vehicle through analyzing the input of historical trajectory information or its control signals. In actual traffic scenarios, the movement between vehicles is a process of mutual game and cooperation, namely the future trajectory of a vehicle is not only related to its own historical trajectory, but also to surrounding vehicles motion. However, different surrounding traffic participants have different influence on the target vehicle, and the future motion of the vehicle is often affected by some specific surrounding traffic agents deeply.

Multiple object detection and tracking are central aspects of modeling the environment of autonomous vehicles. Lidar is a necessary component in the autonomous driving system. Without Lidar sensors, we will most probably not see fully self-driving cars become a reality. Lidar sensing gives us high-resolution data by sending out thousands of laser signals. In advanced driver assistance systems or automated driving systems, 3-D point clouds from lidar scans are typically used to measure physical surfaces. Lidar is a powerful sensor that you can use in challenging environments where other sensors might prove inadequate. Lidar can provide a complete 360-degree view of a scene. This paper designs Lidar based multi-target detection and tracking system based on the traditional point cloud processing method including down-sampling, denoising, segmentation, and clustering objects.

With the widespread application of autonomous driving technology, occupant comfort has become a key topic. Occupant comfort of autonomous vehicles depends on the driving system’s performance, so studying the causes of occupant discomfort will help design driving systems. In addition to the discomfort in NVH and thermal comfort, occupant comfort is also affected by other factors such as safety perception. To study the impact of safety perception on comfort, this paper designed a road experiment and focused on the overtaking scenarios. Because the interaction between the ego vehicle and others is strong during overtaking, the occupants are more likely to receive visual stimuli, resulting in discomfort caused by safety perception. In the experiment, occupant discomfort scores were collected in real-time by the tool developed in this paper.

Recent researches in autonomous driving mainly consider the uncertainty in perception and prediction modules for safety enhancement. However, obstacles which block the field-of-view (FOV) of sensors could generate blind areas and leaves environmental uncertainty a remaining challenge for autonomous vehicles. Current solutions mainly rely on passive obstacles avoidance in path planning instead of active perception to deal with unexplored high-risky areas. In view of the problem, this paper introduces the concept of information entropy, which quantifies uncertain information in the blind area, into the motion planning module of autonomous vehicles. Based on model predictive control (MPC) scheme, the proposed algorithm can plan collision-free trajectories while actively explore unknown areas to minimize environmental uncertainty. Simulation results under various challenging scenarios demonstrate the improvement in safety and comfort with the proposed perception-aware planning scheme.

Aiming at the test safety problems in the early stage of self-driving cars development, firstly the virtual vehicle on-board CAN data acquisition module of the present project was designed based on virtual LabVIEW. Then a wireless remote control system for the self-driving car was constructed, which integrated the built virtual vehicle on-board CAN data acquisition system, the remote real-time image monitoring module and the remote upper computer control module based on ZigBee wireless transmission. It can execute the environmental awareness training and continuous and complex motion manipulation testing of the vehicle without relying on the driver, which can solve the safety problems in the tests of initial development of self-driving cars. Finally, the four-wheel independent steering electric vehicle was used as the self-driving test vehicle, and the wireless remote control system was tested on the double lane change type path and S-type path.

A tractor-trailer vehicle (TTV) consists of an actuated tractor attached with several full trailers. Because of its nonlinear and noncompleted constraints, it is a challenging task to avoid collisions for path planner. In this paper, we propose an efficient method to plan an optimal trajectory for TTV to reach the destination without any collision. To deal with the complicated constraints, the trajectory planning problem is formulated as an optimal control problem uniformly, which can be solved by the interior point method. A novel incremental optimization solving algorithm (IOSA) is proposed to accelerate the optimization process, which makes the number of trailers and the size of obstacles increase asynchronously. Simulation experiments are carried out in two scenarios with static obstacles. Compared with other methods, the results show that the planning method with IOSA outperforms in the efficiency.

The mechanism of automobile brake hot spots is unclear, which is a problem in the brake industry. Complex coupling between friction, heat, contact, and structure is the main difficulty in revealing the mechanism of brake hot spots. This paper proposes a new way to study the mechanism of hot spots by analyzing the deformation behavior of brake discs under asymmetric mechanical loading. The actual brake is simplified into a brake disc and friction lining system, and a transient dynamic finite element model under asymmetric mechanical loads is established to analyze the deformation characteristics of the brake disc. The normal deformation of the brake disc under asymmetric mechanical loads consists of two parts: low-frequency bending deformation and high-frequency waviness deformation, which are caused by the squeezing effect of the asymmetric brake pressure on the brake disc and the constraint modal vibration of the brake disc.

Motivated by applying artificial intelligence technology to the automobile industry, reinforcement learning is becoming more and more popular in the community of intelligent driving research. The reward function is one of the critical factors which affecting reinforcement learning. Its design principle is highly dependent on the features of the agent. The agent studied in this paper can do perception, decision-making, and motion-control, which aims to be the assistant or substitute for human driving in the latest future. Therefore, this paper analyzes the characteristics of excellent human driving behavior based on the six-layer model of driving scenarios and constructs it into a human knowledge graph. Furthermore, for highway pilot driving, the expert demo data is created, and the reward function is self-learned via inverse reinforcement learning. The reward function design method proposed in this paper has been verified in the Unity ML-Agent environment.

Tracking vehicle trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar polar coordinate system.

Autonomous vehicle performance is increasingly highlighted in many highway driving scenarios, which leads to more priorities to vehicle stability as well as tracking accuracy. In this paper, a nonlinear model predictive controller for autonomous vehicle trajectory tracking is designed and verified through a real-time simulation bench of a virtual test track. The dynamic stability constraints of nonlinear model predictive control (NLMPC) are obtained by a novel quadrilateral stability region criterion instead of the conventional phase plane method using the double-line region. First, a typical lane change scene of overtaking is selected and a new composited trajectory model is proposed as a reference path that combines smoothness of sine wave and comfort of linear functional path. Reference lateral velocity, azimuth angle, yaw rate, and front wheel steering angle are subsequently taken into account.

Automatic Lane Change (ALC) function is an important step to promote the currently popular Advanced Driver Assistance Systems (ADAS) within a single lane. The key issue for ALC is accurate steering angle and trajectory tracking during the lane changing process. In this paper, an MPC controller with a receding horizon is designed to track the desired trajectory. During the tracking process, other objectives such as safety and smoothness are considered. Considering of the practical mechanism and parameter uncertainties, an SMC controller is designed to track the target steering angle. For validation, a Hardware-in-the-Loop (HIL) experiment platform is established, and experiments of different control algorithms under different conditions are carried out successively. Comparisons of the experiment results of MPC+SMC and PID+SMC schemes indicate that both the trajectory error and the steering angle error of the former combination are smaller.

For the personification of automotive vehicle function performance under common traffic scenarios, analysis of human driver behavior is necessary. Based on China Field Operational Test (China-FOT) database of China Natural Driving Study project, this paper studies the driver's response in the common cut-in scenario. A total of 266 cut-in cases are selected by manual interception of driving recorder video. The relevant traffic environment characteristics are also extracted from video, including light conditions, road conditions, scale and lateral position of cut-in vehicle, etc. Dynamic information is decoded form CAN, such as speed, acceleration and so on. Then image processing results, such as relative speed and distance of cut-in and subject vehicles, are calculated. Statistical results based on above information show the response type and distribution of human driver: the behavior of keeping lane is 96.24%, in which the ratio of braking response is 51.13%.

A composite steering control strategy, which combines four-wheel steering (4WS) and differential steering, is proposed in this paper, to optimize steering maneuverability in the conditions where the vehicle speed is below 15 Km/h, mainly for U-turning and parking conditions. A dynamic model is developed for the steering system and the tire system. Taking different steering wheel inputs into consideration, a 4WS control strategy proportional to the front wheel steering angle is quoted to improve the steering maneuverability in the low speed conditions and guarantee the manipulability by controlling the side slip of the vehicle. Based on the 4WS system, this paper explores the possibility of further improving the low-speed maneuverability of the vehicle through differential steering. And the differential steering control strategy is developed, including four hub-motor output modes. A composite steering controller is designed based on the 4WS-4WD electric vehicle platform.