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

2025-05-01

AI Summary

Conventional active safety analysis suffers from limited accuracy in complex traffic scenarios due to inadequate modeling of multi-vehicle interactions. Method: This paper proposes an interaction-aware active safety analysis framework that integrates a grade-compensated bicycle dynamics model with a hypergraph neural network to explicitly capture both inter-vehicle interactions and dynamic coupling with road geometry. It jointly solves stochastic differential equations governing longitudinal vehicle spacing in 3D space, generating probabilistic trajectory ensembles and high-fidelity HF-TTC distributions via fourth-order stochastic Runge–Kutta integration. Contribution/Results: For the first time, this work unifies group-level interaction modeling, road-constrained dynamics, and stochastic vehicle motion within a single active safety assessment framework—overcoming traditional assumptions of constant speed and no interaction. Evaluated on a highway dataset, the method achieves significantly higher TTC prediction fidelity than baseline approaches, substantially enhancing situational awareness under multi-agent strategic interactions and behavioral uncertainty.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Analyzes groupwise vehicle interactions for active safety using AI
• Integrates vehicle dynamics and hypergraph-based trajectory prediction
• Enhances safety perception in complex traffic with high-fidelity TTC

Innovation

• AI-enabled interaction-aware safety framework
• Hypergraph-based AI for trajectory prediction
• Stochastic ODE for high-fidelity safety measures