Adaptive Kalman-based hybrid car following strategy using TD3 and CACC
This addresses safety and performance issues in autonomous driving for mixed traffic flows, representing an incremental improvement over existing hybrid strategies.
The paper tackles the challenge of adapting hybrid car following strategies to mixed traffic flow scenarios by proposing an adaptive Kalman Filter-based approach that combines CACC and TD3 algorithms. Simulation results over 4,157,745 timesteps show the method substantially enhances safety without compromising comfort and efficiency compared to TD3 and HCFS algorithms.
In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and significantly improve the performance of car following. However, it is challenging for the traditional hybrid strategy based on fixed coefficients to adapt to mixed traffic flow scenarios, which may decrease the performance and even lead to accidents. To address the above problems, a hybrid car following strategy based on an adaptive Kalman Filter is proposed by regarding CACC and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. Different from traditional hybrid strategy based on fixed coefficients, the Kalman gain H, using as an adaptive coefficient, is derived from multi-timestep predictions and Monte Carlo Tree Search. At the end of study, simulation results with 4157745 timesteps indicate that, compared with the TD3 and HCFS algorithms, the proposed algorithm in this study can substantially enhance the safety of car following in mixed traffic flow without compromising the comfort and efficiency.