Kavli Affiliate: Jing Wang
| First 5 Authors: Jing Wang, Jing Wang, , ,
| Summary:
Since the advent of autonomous driving technology, it has experienced
remarkable progress over the last decade. However, most existing research still
struggles to address the challenges posed by environments where multiple
vehicles have to interact seamlessly. This study aims to integrate causal
learning with reinforcement learning-based methods by leveraging causal
disentanglement representation learning (CDRL) to identify and extract causal
features that influence optimal decision-making in autonomous vehicles. These
features are then incorporated into graph neural network-based reinforcement
learning algorithms to enhance decision-making in complex traffic scenarios. By
using causal features as inputs, the proposed approach enables the optimization
of vehicle behavior at an unsignalized intersection. Experimental results
demonstrate that our proposed method achieves the highest average reward during
training and our approach significantly outperforms other learning-based
methods in several key metrics such as collision rate and average cumulative
reward during testing. This study provides a promising direction for advancing
multi-agent autonomous driving systems and make autonomous vehicles’ navigation
safer and more efficient in complex traffic environments.
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