Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network

TL;DR

This paper presents a deep reinforcement learning approach using Deep Recurrent Q-Networks for urban autonomous driving decision-making in pedestrian-rich environments, outperforming rule-based methods in complex simulations.

Contribution

It introduces a DRQN-based decision-making framework tailored for urban driving with pedestrians, incorporating a 3-D state representation and memory capabilities.

Findings

DRQN outperforms rule-based approaches in urban scenarios

Memory integration via LSTM improves decision-making in complex environments

Proposed method effectively handles dense urban pedestrian interactions

Abstract

Decision making for autonomous driving in urban environments is challenging due to the complexity of the road structure and the uncertainty in the behavior of diverse road users. Traditional methods consist of manually designed rules as the driving policy, which require expert domain knowledge, are difficult to generalize and might give sub-optimal results as the environment gets complex. Whereas, using reinforcement learning, optimal driving policy could be learned and improved automatically through several interactions with the environment. However, current research in the field of reinforcement learning for autonomous driving is mainly focused on highway setup with little to no emphasis on urban environments. In this work, a deep reinforcement learning based decision-making approach for high-level driving behavior is proposed for urban environments in the presence of pedestrians. For this, the use of Deep Recurrent Q-Network (DRQN) is explored, a method combining state-of-the art Deep Q-Network (DQN) with a long term short term memory (LSTM) layer helping the agent gain a memory of the environment. A 3-D state representation is designed as the input combined with a well defined reward function to train the agent for learning an appropriate behavior policy in a real-world like urban simulator. The proposed method is evaluated for dense urban scenarios and compared with a rule-based approach and results show that the proposed DRQN based driving behavior decision maker outperforms the rule-based approach.