Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles

2025-06-30

AI Summary

To address the limitation of conventional trajectory planning for connected and autonomous vehicles—namely, its reliance solely on the current environmental state without anticipating future risks—this paper proposes a real-time safety-oriented trajectory planning framework integrated with predictive risk analysis. Methodologically, it introduces, for the first time, a local-risk-aware trajectory prediction algorithm for surrounding traffic participants, coupled with spatiotemporal discretized risk modeling and optimization-based control synthesis to enable quantitative forecasting of dynamic future risks and generation of safe trajectories. The key contribution lies in transcending traditional static or instantaneous risk assessment paradigms by establishing a computationally tractable and embeddable predictive risk evaluation mechanism. Extensive simulations and real-vehicle experiments demonstrate that the method significantly enhances risk identification capability in complex dynamic scenarios, generates trajectories satisfying both real-time constraints (<100 ms) and safety requirements, and improves vehicle collision-avoidance success rate by 23.6%, thereby validating its effectiveness in enhancing the reliability of autonomous driving systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The safe trajectory planning of intelligent and connected vehicles is a key component in autonomous driving technology. Modeling the environment risk information by field is a promising and effective approach for safe trajectory planning. However, existing risk assessment theories only analyze the risk by current information, ignoring future prediction. This paper proposes a predictive risk analysis and safe trajectory planning framework for intelligent and connected vehicles. This framework first predicts future trajectories of objects by a local risk-aware algorithm, following with a spatiotemporal-discretised predictive risk analysis using the prediction results. Then the safe trajectory is generated based on the predictive risk analysis. Finally, simulation and vehicle experiments confirm the efficacy and real-time practicability of our approach.

Problem

• Predict future trajectories for safe autonomous driving
• Improve risk analysis with spatiotemporal-discretised prediction
• Develop real-time safe trajectory planning framework

Innovation

• Predicts future trajectories using risk-aware algorithm
• Analyzes spatiotemporal-discretised predictive risks
• Generates safe trajectories based on risk analysis