Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning

Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning

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This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by Medical and Ambulatory supplies / Over the Counter making use of Machine Learning (ML) and physical knowledge.More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution.In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet Faux Knitting Ugly Sweater size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs).