AI2-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics

TL;DR

This paper presents an AI-enabled framework for interaction-aware active safety analysis that models vehicle dynamics and traffic interactions to produce high-fidelity safety measures like probabilistic time-to-collision estimates.

Contribution

It introduces a novel integration of vehicle dynamics modeling with hypergraph-based AI traffic prediction for improved safety analysis in complex environments.

Findings

High-fidelity probabilistic TTC distributions generated

Outperforms traditional constant-velocity TTC methods

Effectively captures multi-agent maneuvers and uncertainties

Abstract

This paper introduces an AI-enabled, interaction-aware active safety analysis framework that accounts for groupwise vehicle interactions. Specifically, the framework employs a bicycle model-augmented with road gradient considerations-to accurately capture vehicle dynamics. In parallel, a hypergraph-based AI model is developed to predict probabilistic trajectories of ambient traffic. By integrating these two components, the framework derives vehicle intra-spacing over a 3D road surface as the solution of a stochastic ordinary differential equation, yielding high-fidelity surrogate safety measures such as time-to-collision (TTC). To demonstrate its effectiveness, the framework is analyzed using stochastic numerical methods comprising 4th-order Runge-Kutta integration and AI inference, generating probability-weighted high-fidelity TTC (HF-TTC) distributions that reflect complex multi-agent maneuvers and behavioral uncertainties. Evaluated with HF-TTC against traditional constant-velocity TTC and non-interaction-aware approaches on highway datasets, the proposed framework offers a systematic methodology for active safety analysis with enhanced potential for improving safety perception in complex traffic environments.