@article{en14227572,Author = {Jurj, Sorin Liviu and Grundt, Dominik and Werner, Tino and Borchers, Philipp and Rothemann, Karina and Möhlmann, Eike},Title = {Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning},Journal = {Energies},Year = {2021},Doi = {10.3390/en14227572},type = {article},Abstract = {This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by 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 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).}}@COMMENT{Bibtex file generated on }