Predictive Traffic Rule Compliance using Reinforcement Learning

2025-03-29

AI Summary

Existing autonomous driving path planners often lack sufficient compliance with traffic regulations, particularly struggling to predict and avoid out-of-sight violations in complex scenarios. Method: This paper proposes a rule-aware deep reinforcement learning framework that replaces the conventional actor network with an interpretable motion planner; integrates a graph neural network into the actor-critic architecture to encode traffic semantic relationships; and couples a structured rule base—formalized from Germany’s Road Traffic Regulations (StVO)—with a real-time optimized cost function. Contribution/Results: The framework unifies long-horizon regulatory compliance with interpretable trajectory generation. Evaluated on a public German highway dataset, it significantly reduces violations such as lane departures and illegal parking, proactively predicts and avoids out-of-sight infractions, and enhances both safety and regulatory adherence in complex traffic environments.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Autonomous vehicle path planning has reached a stage where safety and regulatory compliance are crucial. This paper presents a new approach that integrates a motion planner with a deep reinforcement learning model to predict potential traffic rule violations. In this setup, the predictions of the critic directly affect the cost function of the motion planner, guiding the choices of the trajectory. We incorporate key interstate rules from the German Road Traffic Regulation into a rule book and use a graph-based state representation to handle complex traffic information. Our main innovation is replacing the standard actor network in an actor-critic setup with a motion planning module, which ensures both predictable trajectory generation and prevention of long-term rule violations. Experiments on an open German highway dataset show that the model can predict and prevent traffic rule violations beyond the planning horizon, significantly increasing safety in challenging traffic conditions.

Problem

• Predicting traffic rule violations using reinforcement learning
• Integrating motion planning with deep learning for compliance
• Ensuring autonomous vehicle safety in complex traffic conditions

Innovation

• Integrates motion planner with reinforcement learning
• Uses graph-based state representation for traffic
• Replaces actor network with motion planning module