| The distributed X-by-wire chassis has higher controllability and flexibility compared to traditional vehicles by integrating drive,steering,braking,and suspension X-by-wire technologies,making it an ideal platform for autonomous driving.Under advanced autonomous driving conditions,autonomous vehicles will completely break away from the dependence on human driver monitoring and intervention,and require autonomous vehicles to have comprehensive,high-performance,safe,and stable driving capabilities.Real-time trajectory planning and precise motion control in emergency obstacle avoidance scenarios are key technologies for ensuring driving safety in achieving high-order autonomous driving processes.How to combine autonomous driving technology with the redundancy characteristics of distributed X-by-wire chassis to provide a more efficient,safe,and intelligent platform for autonomous driving is currently an urgent technical problem that needs to be solved.This study is supported in part by the National Natural Science Foundation of China “Research on Risk Situation Assessment and Integrated Motion Control Method for Intelligent Wire Controlled Chassis Motion Considering Actuator Failure”(Grant No.52372412),the Technology Innovation and Application Development Special Project of Chongqing “Intelligent Driving Simulation Platform for Next-Generation EVs with New Architecture”(Grant No.CSTB2022TIAD-DEX0014).For distributed X-by-wire chassis,emergency obstacle avoidance trajectory planning and coupled motion control methods are designed.The aim of this study is to plan the emergency obstacle avoidance trajectory that safely avoids obstacles and meets the vehicle dynamics constraints in real time,and utilize the output of IMU sensor and the chassis feedback state information to realize the accurate estimation of the key vehicle dynamic parameters.To make full use of the redundancy characteristics of the distributed X-by-wire chassis,it is necessary to design a high-performance coupled motion controller to realize the accurate tracking of emergency obstacle avoidance trajectories,and to give full play to the chassis motion performance of distributed vehicles under the premise of ensuring the vehicle stability.The research content of this study mainly includes the following aspects:(1)For emergency obstacle avoidance scenarios with dynamic obstacles,this study carries out the design of spatiotemporal trajectory planning algorithm.Firstly,according to the obstacles and the surrounding environment information,the construction of 3D spatiotemporal grid map is carried out,and the improved hybrid A* algorithm is utilized for the search of coarse trajectory.According to the boundary of motion capability of distributed X-by-wire chassis,the motion primitives are generated by vehicle acceleration sampling and pruning to improve the search efficiency while ensuring the node scalability.The collision risk is considered in the heuristic function to minimize the ineffective exploration of child nodes with high collision risk.Meanwhile,the One-Shot search algorithm based on the OBVP is designed to improve the search efficiency near the target state.Based on the coarse trajectory obtained from the improved hybrid A* search,the construction of linear safety corridor constraints is achieved to ensure the collision safety of the generated emergency obstacle avoidance trajectory.Using the vehicle model without sideslip as the prediction model,based on the information of the safety corridor and the boundary of motion capability of distributed X-by-wire chassis,the emergency obstacle avoidance trajectory is obtained through the model prediction control algorithm.Finally,this study compares the designed emergency obstacle avoidance trajectory planning algorithm with the EMPlanner algorithm and the trajectory planning algorithm based on nonlinear model predictive control for verification.(2)Aiming at the problems of high cost and limited usage scenarios of high-precision vehicle state measurement equipment,and the problems of steady-state offset and model mismatch in the mechanism model-based vehicle state estimation method,this study designs a fusion estimation method of the key vehicle dynamic parameters driven by a hybrid data-mechanism model.Considering that the vehicle state is typical time series data and the state estimation results based on the mechanism model contain rich information aligned with the actual vehicle state,this study innovatively combines the estimation results of the mechanism model based methods with IMU sensor state detection information and feedback state information of the chassis domain as inputs for data-driven methods,and uses a long short-term memory neural network structure to achieve vehicle lateral velocity estimation.In response to the limitations of data-driven estimation methods on the size of the dataset and the problems of poor generalization and scene adaptability,this study divides the lateral velocity estimation neural network into two parts: feature extraction layer and regression output layer.The parameters of the complex feature extraction layer are fine-tuned,while the focus is on retraining the parameters of the relatively simple structure of the regression output to realize the rapid deployment and migration of the pre-trained model on the target domain using 5～10 minutes training samples.At the same time,this study designs a TPOAM,which uses the estimated lateral velocity as a pseudo measurement value.The dual extended Kalman filter is used to jointly estimate the cornering stiffnesses and vehicle states.Finally,the estimation performance of the key vehicle dynamic parameters driven by the hybrid data-mechanism model proposed,transfer learning based on small samples,and estimation performance of TPOAM module were verified in a simulation environment.(3)For high controllability and redundancy of distributed X-by-wire chassis,this study proposes a hierarchical coupling motion control method.In the motion-following layer,the target global force/moment is calculated based on VTDLTV-MPC.To prevent the deterioration of the control performance caused by the modeling error of the hierarchical controller that the control objectives generated in the upper layer cannot be realized by the lower layer controller,this study designs a hierarchical control characteristic modeling method based on multi-objective optimization with state augmentation and expansion of the prediction model in the motion-following layer.The linearization reference point is updated with the predicted state and control sequence of the last moment to improve the prediction model accuracy,and the time length of the prediction time domain is effectively extended without increasing the computational burden by the variable-time steps discretization method in the prediction domain.In the control-allocation layer,considering the vertical load transfer and the friction circle constraint of each wheel,the quadratic programming algorithm is used to calculate the target driving force of each wheel and the target lateral force of the front and rear axle.In the execution layer,this study utilizes the online updated front and rear axle equivalent cornering stiffnesses from the TPOAM to accurately track the target front and rear axle lateral forces calculated in the control-allocation layer.Finally,the emergency obstacle avoidance trajectory tracking performance,and coupled motion control performance of the proposed method are verified on the HIL platform.(4)The HIL experimental platform for the simulation verification of comprehensive working conditions is built,and utilize the distributed X-by-wire chassis experimental platform to carry out the verification of the estimation of the key dynamic parameters as well as the control performance of the proposed coupled motion controller in real-vehicle experiments.Using Speed Goat,NI PXIe-1071 simulator and IPC to build a joint simulation hardware-in-the-loop experimental platform,the spatiotemporal obstacle avoidance trajectory planning and coupled motion control methods proposed in this study is comprehensively validated in the typical emergency obstacle avoidance scenarios.Considering that there are still differences between the simulation and the real vehicle environment,this study utilizes the distributed X-by-wire chassis experimental platform to collect training samples for the estimation of key parameters of vehicle dynamics,and conducts the training of the vehicle lateral velocity estimation neural network.The effectiveness of the lateral velocity estimation and the estimation of the equivalent cornering stiffnesses of the front and rear axles of the TPOAM is validated in a real vehicle environment.To achieve rapid deployment of neural networks in real vehicle environments,this study conducted transfer learning validation of vehicle lateral velocity estimation neural networks from simulation to real vehicle and from four-wheel steering mode to two-wheel steering mode in real vehicle environments.Finally,the distributed X-by-wire chassis experimental platform is utilized to carry out comparative validation of coupled motion control and validation of the experimental field scenario.The experimental results show that the proposed coupled motion control method effectively controls the vehicle state at the boundary of the friction circle while ensuring the accuracy of path and speed tracking,giving full play to the motion potential of the distributed X-by-wire chassis. |
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