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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.

Tire forces and moments play an important role in vehicle dynamics and safety. X-by-wire chassis components including active suspension, electronic powered steering, by-wire braking, etc can take the tire forces as inputs to improve vehicle’s dynamic performance. In order to measure the accurate dynamic wheel load, most of the researches focused on the kinematic parameters such as body longitudinal and lateral acceleration, load transfer and etc. In this paper, the authors focus on the suspension system, avoiding the dependence on accurate mass and aerodynamics model of the whole vehicle. The geometry of the suspension is equated by the spatial parallel mechanism model (RSSR model), which improves the calculation speed while ensuring the accuracy. A suspension force observer is created, which contains parameters including spring damper compression length, push rod force, knuckle accelerations, etc., combing the kinematic and dynamic characteristic of the vehicle.

The formation is a crucial step in the production process of lithium-ion batteries (LIBs), during which the solid electrolyte interphase (SEI) is formed on the surface of the anode particles to passivate the electrode. It determines the performance of the battery, including its capacity and lifetime. A meticulously designed formation protocol is essential to regulate and optimize the stability of the SEI, ultimately achieving the optimal performance of the battery. Current research on formation protocols in lithium-ion batteries primarily focuses on temperature, current, and voltage windows. However, there has been limited investigation into the influence of different initial pressures on the formation process, and the evolution of cell pressure during formation remains unclear. In this study, a pressure-assisted formation device for lithium-ion pouch cells is developed, equipped with pressure sensors.

As one of the pollutants that cannot be ignored, soot has a great impact on human health, environment, and energy conversion. In this investigation, the effect of residence time (25ms, 35ms, and 45ms) and ammonia on morphology and nanostructure of soot in laminar ethylene flames has been studied under atmospheric conditions and different flame heights (15 mm and 30 mm). The transmission electron microscopy (TEM) and high-resolution transmission electron microscope (HRTEM) are used to obtain morphology of aggregates and nanostructure of primary particles, respectively. In addition, to analyze the nanostructure of the particles, an analysis program is built based on MATLAB software, which is able to obtain the fringe separation distance, fringe length, and fringe tortuosity parameters of primary particles, and has been verified by the multilayer graphene interlayer distance.

This paper presents an integrated modeling approach for real-time discretionary lane-changing decisions by autonomous vehicles, aiming to achieve human-like behavior. The approach incorporates a two-player normal-form game and a novel risk field method. The normal-form game represents the strategic interactions among traffic participants. It captures the trade-offs between lane-changing benefits and risks based on vehicle motion states during a lane change. By continuously determining the Nash equilibrium of the game at each time step, the model decides when it is appropriate to change the lane. A novel risk field method is integrated with the game to model risks in the game pay-offs. The risk field introduces regions along the desired target lane with different time headway ranges and risk weights, capturing traffic participants' complex risk perceptions and considerations in lane-changing scenarios.

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.

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.

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.

Accurate and reliable localization in GNSS-denied environments is critical for autonomous driving. Nevertheless, LiDAR-based and camera-based methods are easily affected by adverse weather conditions such as rain, snow, and fog. The 4D Radar with all-weather performance and high resolution has attracted more interest. Currently, there are few localization algorithms based on 4D Radar, so there is an urgent need to develop reliable and accurate positioning solutions. This paper introduces RIO-Vehicle, a novel tightly coupled 4D Radar/IMU/vehicle dynamics within the factor graph framework. RIO-Vehicle aims to achieve reliable and accurate vehicle state estimation, encompassing position, velocity, and attitude. To enhance the accuracy of relative constraints, we introduce a new integrated IMU/Dynamics pre-integration model that combines a 2D vehicle dynamics model with a 3D kinematics model.

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.

LiDAR and camera fusion have emerged as a promising approach for improving place recognition in robotics and autonomous vehicles. However, most existing approaches often treat sensors separately, overlooking the potential benefits of correlation between them. In this paper, we propose a Cross- Modality Module (CMM) to leverage the potential correlation of LiDAR and camera features for place recognition. Besides, to fully exploit potential of each modality, we propose a Local-Global Fusion Module to supplement global coarse-grained features with local fine-grained features. The experiment results on public datasets demonstrate that our approach effectively improves the average recall by 2.3%, reaching 98.7%, compared with simply stacking of LiDAR and camera.

Positioning system is a key module of autonomous driving. As for LiDAR SLAM system, it faces great challenges in scenarios where there are repetitive and sparse features. Without loop closure or measurements from other sensors, odometry match errors or accumulated errors cannot be corrected. This paper proposes a construction method of LiDAR anchor constraints to improve the robustness of the SLAM system in the above challenging environment. We propose a robust anchor extraction method that adaptively extracts suitable cylindrical anchors in the environment, such as tree trunks, light poles, etc. Skewed tree trunks are detected by feature differences between laser lines. Boundary points on cylinders are removed to avoid misleading. After the appropriate anchors are detected, a factor graph-based anchor constraint construction method is designed. Where direct scans are made to anchor, direct constraints are constructed.

In order to improve the efficiency of safety performance test for intelligent vehicles and construct the test case set quickly, critical scenarios based on graded hazard disposal model of human drivers are proposed, which can be used for extraction of test cases for safety performance. Based on the natural driving data in China Field Operational Test (China-FOT), the four-stage collision avoidance process of human drivers is obtained, including steady driving stage, risk judgment stage, collision reaction stage and collision avoidance stage. And there are two human driver states: general state and alert state. Then the graded hazard disposal model of human drivers is constructed.

With the extension of intelligent vehicles from individual intelligence to group intelligence, intelligent vehicle platoons on intercity highways are important for saving transportation costs, improving transportation efficiency and road utilization, ensuring traffic safety, and utilizing local traffic intelligence [1]. However, there are several problems associated with vehicle platoons including complicated vehicle driving conditions in or between platoon columns, a high degree of mutual influence, dynamic optimization of the platoon, and difficulty in the cooperative control of lane change. Aiming at the dual-column intelligent vehicle platoon control (where “dual-column” refers to the vehicle platoon driving mode formed by multiple vehicles traveling in parallel on two adjacent lanes), a multi-agent model as well as a cooperative control method for lane change based on null space behavior (NSB) for unmanned platoon vehicles are established in this paper.

Hydrogen energy is a kind of secondary energy with an abundant source, wide application, green, and is low-carbon, which is important for building a clean, low-carbon, safe, and efficient energy system and achieving the goal of carbon peaking and being carbon neutral. In this paper, the effect of nozzle position, hydrogen injection timing, and ignition timing on the in-cylinder combustion characteristics is investigated separately with the 13E hydrogen engine as the simulation object. The test results show that when the nozzle position is set in the middle of the intake and exhaust tracts (L2 and L3), the peak in-cylinder pressure is slightly higher than that of L1, but when the nozzle position is L2, the cylinder pressure curve is the smoothest, the peak exothermic rate is the lowest, and the peak cylinder temperature is the lowest.

The design method for the powertrain mounting system in internal combustion engine vehicles is well-established. Electric vehicles experience higher vibration frequencies and more significant transient responses when accelerating or braking than fuel vehicles due to their high speed and fast response. Therefore, the design of the electric drive assembly mounting system requires further development. The modeling of electric drive assembly mounting systems often neglects the mounting bracket’s influence, which significantly affects the center of mass and rotational inertia of the electric drive assembly. This paper examines the effect of the mounting bracket in the electric drive assembly mounting system. It establishes a mathematical model with six degrees of freedom for the mounting system, considering the mounting bracket. By comparing the natural characteristics and the transient response, it is discussed whether the mass of the mounting bracket greatly influences the system.

Optimization design of hard point parameters for hinge mechanism has been paid more attention in recent years, attributable to their significant improvement in dynamic performance. In this paper, the experimental analysis and dynamic optimization design of hinge mechanism is performed. The acceleration measurement experiments are carried out at different arrangement points and under different working conditions. Furthermore, the accuracy of established multi-body dynamics model is verified by three-axis accelerometer measurement experiment. In addition, sensitivity analysis for electric strut and gas strut coordinates is performed and shows that the Y coordinate of the lower end point of the electric strut is the design variable that has the greatest impact on the responses.

The displacement of the shaft head fails to be accurately measured while the three-axle heavy-duty truck is driving on the reinforced pavement. In order to obtain accurate fatigue load spectrum of the suspension bracket, the acceleration signals of the shaft heads of the suspension obtained by the reinforced pavement test measurement are virtually iterated as responses. A more accurate model of the rigid-flexible coupled multi-body dynamics (MBD) of the whole vehicle is established by introducing a flexible frame based on the comprehensive modal theory. Furthermore, the vertical displacements of the shaft heads are obtained by the reverse solution of the virtual iterative method with well-pleasing precision. The accuracy of the virtual iteration is verified by comparing the simulation results with the vertical acceleration of the shaft head under the reinforced pavement in the time domain and damage domain.

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.

To improve the maneuverability and energy consumption of an electrical vehicle, a two-level speed control method based on model predictive control (MPC) is proposed for accurate control of the vehicle during downhill coasting. The targeted acceleration is planned using the anti-interference speed filter and MPC algorithm in the upper-level controller and executed using the integrated algorithm with the inverse vehicle dynamics and proportional-integral-derivative control model (PID) in the lower-level controller, improving the algorithm’s anti-interference performance and road adaptability. Simulations and vehicle road tests showed that the proposed method could realize accurate real-time speed control of the vehicle during downhill coasting. It can also achieve a smaller derivation between the actual and targeted speeds, as well as more stable speeds when the road resistance changes abruptly, compared with the conventional PID method.