Autonomous vehicle requires a high-precision lateral dynamics model for path following and lateral stability control. However, existing physical models suffer from low accuracy due to modeling simplification and inaccurate model parameters, while data-driven models lack physical interpretability and robustness. To address these issues, a hybrid architecture inspired by vehicle lateral dynamics is developed in this study, which embeds the data-driven model into a physical model for parameter identification and error characterization to achieve accurate and interpretable modeling. Specifically, a physical lateral dynamics model with error analysis is established at first, and the problems of modeling error characterization and parameter identification are formulated. Then, the physical lateral dynamics model is deformed, and the modeling errors and cornering stiffness are unified into compound parameters. Using this deformed physical model, the modeling errors can be characterized by the identification of these compound parameters. To obtain high-precision compound parameters, a neural network-based parameter identification method is proposed, and the identified time-varying parameters enable high-precision characterization of modeling errors and parameters using data knowledge. By embedding the neural network into the deformed physical model, a hybrid model integrating physical laws and data knowledge is finally established for the description of vehicle lateral dynamics. Simulation and experimental results demonstrate that the proposed hybrid model realizes more accurate modeling of vehicle lateral dynamics than conventional physical and data-driven models.

An accurate longitudinal dynamics model is essential for state estimation and control of autonomous vehicles. However, existing physical models suffer from limited working conditions and large errors, while pure data-driven models require massive amounts of driving data to cover working conditions fully. To address these issues, a hybrid architecture composed of a neural network-based traction model, a recursive least square-based parameter estimator, and a physics-based dynamics model is proposed for longitudinal dynamics modeling, in which the parameter estimator is used for mass and modified rolling friction coefficient estimation. Under this architecture, the longitudinal dynamics model can be established using limited driving data collected on a test field with a given load, and achieve precise vehicle dynamics characterization under various roads and loads. To design the neural network for traction description, the dynamics of vehicle powertrain and braking systems are analyzed, and a physics-guided neural network, which fully considers the traction transmission characteristics, is formulated. For model training, a two-stage hybrid model training method is proposed, which can train the hybrid model with the co-existence of unknown network and physical parameters. Results demonstrate that the proposed hybrid model can realize accurate parameter estimation and vehicle longitudinal dynamics modeling using limited driving data collected at a test field under a given load, especially with excellent generalization performance under different loads and roads.

In mobile robot path planning, the traditional A* algorithm suffers from high path redundancy and poor smoothness, while the Dynamic Window Approach (DWA) tends to deviate from the global optimal path and has low efficiency in avoiding dynamic obstacles when integrated with global path planning. To address these issues, a smoothing optimised A*-guided DWA fusion algorithm (SOA-DWA) is proposed in this paper. Firstly, the A* algorithm was improved by introducing a path smoothing strategy and path pruning mechanism, generating a globally optimal path that complied with the vehicle kinematic constraints. Secondly, three sub-functions were introduced into the evaluation function of the DWA algorithm: the distance evaluation between the reference trajectory and the global path, the path direction evaluation, and the dynamic obstacle avoidance evaluation, to enhance the real-time performance of dynamic obstacle avoidance and the consistency of the global path. The SOA-DWA algorithm ensured that the mobile robot could effectively avoid obstacles in complex environments without deviating from the global optimal path. Thirdly, experimental results show that in a static environment, the path length and turning angle of the SOA-DWA algorithm are reduced by an average of 13.3% and 16.25%, respectively, compared with the traditional algorithm. In a dynamic environment, the path length and turning angle are reduced by an average of 10.5% and 14.5% compared to the traditional DWA algorithm, respectively, significantly improving the smoothness of the path and driving safety. Compared to the existing fusion algorithm, the SOA-DWA algorithm reduces the path length by an average of 10.1%, improves planning efficiency by an average of 42%, and effectively enhances obstacle avoidance efficiency. Finally, the effectiveness of the improved algorithm proposed in this paper was further verified by mobile robot experiments.

The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is significantly simplified by this system’s capability to swiftly and accurately generate a vast array of specific scenarios through the input of scene parameters. The results indicate that this system achieves SG at a rate 2.5-fold faster than manual methods, alongside substantial improvements in accuracy. This system introduces a novel approach to virtual simulation, which is vital for the progress of autonomous driving safety. It offers a new paradigm for quickly and precisely generating test scenarios for autonomous driving.

Dynamic performances of the vehicle are significantly influenced by the suspension mechanisms. An understanding of the effects of the suspension kinematics and statics (or, briefly, kinestatics) is crucial to improve the dynamic performances of a vehicle. However, the suspension kinestatics is often neglected in the dynamic analysis. This paper presents a generalized full-vehicle model for the three-dimensional dynamic analysis, which consists of two pairs of the front and rear spatial suspension mechanisms. Each suspension is represented by a corresponding instantaneous screw joint supporting the vehicle body at any instant. The full-vehicle model is viewed to be a 6-degree-of-freedom spatial parallel mechanism. As the spatial parallel mechanism, the kinematics and statics of the full-vehicle model are analysed using the theory of screws. Taking the suspension kinestatics and tyre dynamics into consideration, the dynamic equations of the full-vehicle model are formulated in terms of the Lagrangian equations. As immediate applications, the dynamic behaviours of a vehicle are simulated and evaluated under two different road disturbances, respectively. By comparing with the simulation results from two other widely used methods, it confirms the validity of the theoretical method.

The dynamic characteristics of a vehicle are significantly influenced by the suspension mechanism. In this paper, the nonlinear kinestatic relations of a planar suspension mechanism are taken into account in the dynamic analysis of a vehicle. A planar suspension mechanism can be considered a 1-DOF parallel mechanism. The Jacobian is used for the kinestatic analysis of the suspension. The motions of the suspension can be represented by instantaneous screw. Based on these kinematic and static relations, the dynamic performances of a quarter-vehicle model with a planar suspension mechanism are described in terms of Lagrangian equations. Finally, as illustrated in the examples, two different kinds of road disturbances are inputted into the wheel. The dynamic responses of a quarter-vehicle model are simulated and compared with the simulation software Adams/View for the validity of the theoretical method.