Vehicle Handling Inverse Dynamics Modeling And Analysis While Encountering An Emergency Collision Avoidance

The character of vehicle handling dynamics affects vehicle handling handiness, and is a main performance index determining safe running for high speed vehicle. In the case of vehicle high speed turning, driver model parameters are not easy to determine, which makes driver model difficult to build. In order to avoid the problem, driver handling input is calculated by the handling inverse dynamics method. Vehicle handling inverse dynamics modeling and analysis while encountering an emergency collision avoidance is researched and the main purposes of this paper are:(1)A three-degree-freedom closed-loop system for steering angel input vehicle model is established. Using an evaluation index which has contains path error, steering wheel angular velocity and lateral acceleration to choose the optimal preview time of each condition. The simulation result is consistent with test data, it is verified the correctness of the closed-loop system.(2) Using the established driver-vehicle three-degree-freedom closed-loop system to calculate the steering wheel manipulate information of a model in different lane change conditions. The changing law are also summarized so that the dynamic characteristic of vehicle steering control. Correctness of the summarized changing law of steering control is verified through real vehicle test. Based on the formal analysis results, the evaluation indexes of dynamic characteristic of vehicle steering control are proposed exploratory. Calculating another model’s dynamic characteristic of vehicle steering control and comparing with the formal model. The second model may be driven easier than the first one. The correctness of evaluation results and the feasibility of the proposed evaluation indexes are verified through contact the manipulating performance of those two models.(3) Based on Elman network, a new dynamic neural network structure, namely state delay input dynamic recurrent neural network(SDIDRNN), was used to improve neural network’s learning speed and convergence precision of the vehicle handling inverse dynamic model recognition. Firstly, the three-degree-of-freedom driver-vehicle closed-loop model was established and the model’s accuracy was also verified by real vehicle test data. Then, the vehicle handling inverse dynamics model was recognized by building the SDIDRNN network model and taken the closed-loop model’s simulation results as the training sample. The obtained results and the error analysis showed the neural network’s learning superiority and the model identification’s effectiveness.(4) Fuzzy control method was suitable for difficult to establish a precise mathematical mode. Fuzzy control theory was introduced into the vehicle handling inverse dynamics. The fuzzy controller of path tracking was designed with considering lateral deviation and rate of change. The driving conditions was obtained by simulating path tracking according to given the double lane change and the serpentine path. The results show that vehicle can well track the expected path in high speed, be able to identity the steering angel input during the whole movement process, be able to compare the maneuverability of different vehicles tracking the same path in high speed, and have good consistency with the results of ADAMS/CAR simulation and Neural network to identify.(5)Vehicle driving safety is the urgent key problem while overtaking with high speed. Minimum time overtaking of vehicle handling inverse dynamics is researched. This note deals with the three-phase overtaking maneuver which includes diverting from the Lane, surpassing, and returning to the lane three stages. Through formulating optimal model, trajectory and time of overtaking is calculated. The simulation results show that the inverse dynamics method for solving problems with vehicles overtaking minimum time maneuver problem, can get the maneuver ability that complete overtaking with the minimum time.