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arXiv · 2407.02546

Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors

Dinesh Cyril Selvaraj·Christian Vitale·Tania Panayiotou·Panayiotis Kolios·Carla Fabiana Chiasserini·Georgios Ellinas
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Abstract

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.

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Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou, Panayiotis Kolios, Carla Fabiana Chiasserini, Georgios Ellinas. 2024-07-02. Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors. https://arxiv.org/abs/2407.02546

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