In this contribution we propose a reinforcement learning-based controller which is able to solve the path following problem for vehicles with significant delay in the drivetrain. To efficiently train the controller, a control-oriented simulation model for a vehicle with combustion engine, automatic gear box and hydraulic brake system has been developed. In addition, to enhance the reinforcement learning-based controller, we have introduced preview information in the feedback state to better deal with the delays. We present our approach of designing a reward function which enables the reinforcement learning-based controller to solve the problem. The controller is trained using the Soft Actor-Critic algorithm by incorporating the developed simulation model. Finally, the performance and robustness is evaluated in simulation. Our controller is able to follow an unseen path and is robust against variations in the vehicle parameters, in our case an additional payload.

Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were trained using the proximal policy optimization algorithm. A validation by comparison with a model-based controller was performed. The performance of the trained controllers was verified with respect to the mean energy consumption and the root mean squared distance error. In our evaluation scenarios, the learning-based controllers reduced energy consumption in comparison to the model-based controller by 17.9% on average.

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