Selvaraj, Dinesh Cyril (CARS@Polito, Politecnico di Torino), Vitale, Christian (University of Cyprus), Panayiotou, Tania (Univeristy of Cyprus), Kolios, Panayiotis (University of Cyprus), Chiasserini, Carla Fabiana (Politecnico di Torino), Ellinas, Georgios (University of Cyprus)

Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors

Scheduled for presentation during the Regular Session "Driver Assistance Systems I" (FrAT11), Friday, September 27, 2024, 10:30−10:50, Salon 19

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

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.