Driving through complex urban environments is a challenging task for autonomous vehicles (AVs), as they must safely reach their mission goal, and react properly to traffic participants while obeying traffic rules. Deep reinforcement learning (DRL) is a promising method to generate driving policies for AVs because it can explore complex environments and learn suitable reactions. In this work, we present a DRL algorithm for AVs to handle intersection scenarios while considering traffic rules. Furthermore, we enhance the safety of our DRL algorithm's decisions by introducing a safety checker based on a responsibility-sensitive safety (RSS) model. Evaluations show that our DRL algorithm outperforms the baseline method by driving safely to reach the mission goal while obeying the traffic rules at an intersection.
Safe and Rule-Aware Deep Reinforcement Learning for Autonomous Driving at Intersections
2022-10-08
1206218 byte
Conference paper
Electronic Resource
English
Autonomous Driving with Deep Reinforcement Learning
SLUB | 2023
|Taylor & Francis Verlag | 2024
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