Abstract
While optimization-based approaches still enjoy mainstream appeal in solving motion planning and control problems, learning-based approaches have become increasingly popular with recent developments in artificial intelligence. Even though current state-of-the-art learning-based approaches to planning and control have their limitations, we feel they will become extremely important in the future and that, as technical trends, they should not be overlooked. More particularly, reinforcement learning has been widely used in solving problems that take place in the form of rounds or time steps with stepwise guiding information such as rewards. Therefore, it has been experimented with as a methodology to solve different levels of autonomous driving planning and control problems. We thus conclude that reinforcement learning-based planning and control will gradually become a viable solution to autonomous driving planning and control problems or at least become a necessary complement to current optimization approaches.
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Liu, S., Li, L., Tang, J., Wu, S., Gaudiot, JL. (2020). Reinforcement Learning-Based Planning and Control. In: Creating Autonomous Vehicle Systems. Synthesis Lectures on Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-031-01805-3_7
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DOI: https://doi.org/10.1007/978-3-031-01805-3_7
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