Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving
Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving
- New search for: Acquarone, Matteo
- New search for: Borneo, Angelo
- New search for: Misul, Daniela Anna
- New search for: Acquarone, Matteo
- New search for: Borneo, Angelo
- New search for: Misul, Daniela Anna
The transportation sector is seeing the flourishing of one of the most interesting technologies, autonomous driving (AD). In particular, Cooperative Adaptive Cruise Control (CACC) systems ensure higher levels both of safety and comfort, enhancing at the same time the reduction of energy consumption. In this framework a real-time velocity planner for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain good results on standard cycles, such as WLTP, UDDS and AUDC, and on real-world driving cycles. Moreover, it displays great adaptability to driving cycles different from the training one.
Document information
Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving
2022-06-15
1878125 byte
Conference paper
Electronic Resource
English
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