Context-aware Risk Assessment and Its Application in Autonomous Driving

TL;DR

This paper introduces the Context-aware Risk Index (CRI), a modular framework for real-time, interpretable risk assessment in autonomous driving that improves safety metrics significantly with minimal computational overhead.

Contribution

The paper presents CRI, a novel, lightweight, and adaptive risk assessment framework that integrates spatial, kinematic, and probabilistic data for enhanced autonomous vehicle safety.

Findings

19% reduction in vehicle collisions per failed route

20% reduction in collisions per kilometer

17% increase in driving score

Abstract

Ensuring safety in autonomous driving requires precise, real-time risk assessment and adaptive behavior. Prior work on risk estimation either outputs coarse, global scene-level metrics lacking interpretability, proposes indicators without concrete integration into autonomous systems, or focuses narrowly on specific driving scenarios. We introduce the Context-aware Risk Index (CRI), a light-weight modular framework that quantifies directional risks based on object kinematics and spatial relationships, dynamically adjusting control commands in real time. CRI employs direction-aware spatial partitioning within a dynamic safety envelope using Responsibility-Sensitive Safety (RSS) principles, a hybrid probabilistic-max fusion strategy for risk aggregation, and an adaptive control policy for real-time behavior modulation. We evaluate CRI on the Bench2Drive benchmark comprising 220 safety-critical scenarios using a state-of-the-art end-to-end model Transfuser++ on challenging routes. Our collision-rate metrics show a 19\% reduction (p = 0.003) in vehicle collisions per failed route, a 20\% reduction (p = 0.004) in collisions per kilometer, a 17\% increase (p = 0.016) in composed driving score, and a statistically significant reduction in penalty scores (p = 0.013) with very low overhead (3.6 ms per decision cycle). These results demonstrate that CRI substantially improves safety and robustness in complex, risk-intensive environments while maintaining modularity and low runtime overhead.