Autonomous vehicles improve the safety and efficiency of vehicles in complex traffic scenarios through autonomous decision-making intelligence technology. To address the requirements of the self-driving vehicle lane change scenario for the accuracy of vehicle lane change trajectory prediction, in this paper, we propose a lane change trajectory prediction method for self-driving vehicles based on inverse reinforcement learning. We model the inverse reinforcement learning process through a maximum entropy mechanism to learn the optimal reward function that infers the potential end targets during the vehicle lane change. This reward model is used to construct the optimal policy that can be sampled for planning in the grid world. Conditioned on the sequence of state actions sampled by this maximum entropy policy, we generate vehicle lane change prediction trajectories. We conduct training experiments on lane change scenario data from the publicly available nuScenes dataset for autonomous driving, which shows that our method can meet the vehicle lane change requirements in real scenarios and validate the accuracy and reasonableness of the lane change trajectories.
In order to better balance the driving safety and ride comfort of the car, a new two-stage semi-active suspension structure is designed with three elements of variable inerter, spring and damper as the suspension structure. Firstly, the ISD semi-active suspension is set up in Matlab/Simulink; Besides both the road model and fuzzy controller model are set up to simulate the dynamics of three schemes, namely passive ISD suspension, inerter PID control and inerter fuzzy control respectively. The results indicate that the performance of ISD suspension adopting the fuzzy control strategy is better, compared with the passive ISD suspension and the ISD PID-controlled suspension. thus it verifies the superiority of using fuzzy control strategy.
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