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

TL;DR

This paper introduces a predictive risk analysis framework for autonomous vehicle trajectory planning that forecasts future environment risks to enhance safety and real-time decision-making.

Contribution

It presents a novel framework combining future trajectory prediction with risk assessment for safer autonomous vehicle navigation.

Findings

Simulation results demonstrate improved safety margins.

Vehicle experiments confirm real-time applicability.

Predictive risk analysis outperforms current methods.

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.