VSG: Rapid Adaptation in Autonomous Driving via Vehicle Skill Graph

The ability to rapidly adapt to unseen scenarios for safe driving has long been a core challenge in autonomous driving. Rule-based methods are heavily reliant on labeled data and suffer from data biases. Many current reinforcement learning(RL)-based methods, on the other hand, are restricted to certain training scenarios, making it difficult for them to adapt to more complex and diverse traffic scenarios. In contrast, human drivers can quickly adapt to new driving situations based on their accumulated driving skills. Inspired by this, we propose the Vehicle Skill Graph (VSG), a novel framework for autonomous driving decision-making. By accumulating 1,000 diverse skills using RL and adopting knowledge graph embedding (KGE) techniques, we build a skill graph that offers a structured understanding of driving knowledge and discovers the potential relations between driving skills and new traffic scenarios. This enables rapid adaptation to new driving environments and addresses the issue of training scenario dependency in RL learning-based approaches. Experimental results demonstrate that VSG effectively captures the latent connections between traffic scenes and driving skills, facilitating efficient sequential decision-making in complex driving situations.