Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways

TL;DR

This paper presents a novel prediction-based approach for real-time collision risk assessment on highways, utilizing probabilistic vehicle state propagation and collision probability calculation to improve safety system responsiveness.

Contribution

It introduces a stochastic forward reachable set and a multi-modal probabilistic acceleration prediction model for collision risk assessment in highway scenarios.

Findings

The prediction model achieves superior accuracy in vehicle position estimation.

The collision detection method effectively identifies cut-in crash events.

Simulation results validate the approach's agility and effectiveness.

Abstract

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We then develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states. The collision probability is calculated by summing up the probabilities of the states where two vehicles spatially overlap. Simulation results show that the prediction model has superior performance in terms of vehicle motion position errors, and the proposed collision detection approach is agile and effective to identify the collision in cut-in crash events.