Abstract
This paper focuses on developing a Deep Reinforcement Learning (DRL)—based agent for real-time trajectory planning and tracking in a simulated parking environment, specifically low-speed maneuvers in a parking area with comb-shaped spaces and a random distribution of non-player vehicles. We rely on CARLA as a virtual driving simulator due to its realistic graphics and physics simulation capabilities, and on the Gymnasium and Stable-Baselines3 toolkits for training the agent. We show that the agent is able to achieve a success rate of 97% in reaching the target parking lot without collisions. However, integrating CARLA with DRL frameworks poses challenges, such as determining suitable environment and neural network update frequencies. Despite these issues, the results demonstrate the potential of DRL agents in developing automated driving functions.
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References
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Acknowledgements
The authors would like to thank all partners within the Hi-Drive project for their cooperation and valuable contribution. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101006664. The sole responsibility of this publication lies with the authors. Neither the European Commission nor CINEA—in its capacity of Granting Authority—can be made responsible for any use that may be made of the information this document contains.
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Lazzaroni, L., Pighetti, A., Bellotti, F., Capello, A., Cossu, M., Berta, R. (2024). Automated Parking in CARLA: A Deep Reinforcement Learning-Based Approach. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_50
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DOI: https://doi.org/10.1007/978-3-031-48121-5_50
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