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In autonomous driving, the hybrid strategy of deep reinforcement learning andcooperative adaptive cruise control (CACC) can fully utilize the advantages ofthe two algorithms and significantly improve the performance of car following.However, it is challenging for the traditional hybrid strategy based on fixedcoefficients to adapt to mixed traffic flow scenarios, which may decrease theperformance and even lead to accidents. To address the above problems, a hybridcar following strategy based on an adaptive Kalman Filter is proposed byregarding CACC and Twin Delayed Deep Deterministic Policy Gradient (TD3)algorithms. Different from traditional hybrid strategy based on fixedcoefficients, the Kalman gain H, using as an adaptive coefficient, is derivedfrom multi-timestep predictions and Monte Carlo Tree Search. At the end ofstudy, simulation results with 4157745 timesteps indicate that, compared withthe TD3 and HCFS algorithms, the proposed algorithm in this study cansubstantially enhance the safety of car following in mixed traffic flow withoutcompromising the comfort and efficiency.

Adaptive Kalman-based hybrid car following strategy using TD3 and CACC