FuzzRisk: Online Collision Risk Estimation for Autonomous Vehicles based on Depth-Aware Object Detection via Fuzzy Inference

2024-11-09

AI Summary

This work addresses the challenge of inaccurate real-time collision risk estimation for autonomous vehicles. We propose a detection-inconsistency-based risk quantification method: safety-critical objects extracted from 2.5D depth maps are cross-modally aligned with outputs from an onboard 3D detector; two inconsistency metrics—IoU overlap and depth deviation—are then computed. A differentiable, interpretable fuzzy inference system maps these inconsistencies to instantaneous collision risk, calibrated against offline collision-rate statistics. Crucially, this is the first approach to explicitly model the relationship between detection inconsistency and ground-truth collision risk. Evaluated on nuScenes, the method demonstrates strong correlation with actual collision events (Spearman’s ρ > 0.82) and reliably triggers risk-responsive behaviors in closed-loop simulation, significantly enhancing system safety.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents a novel monitoring framework that infers the level of collision risk for autonomous vehicles (AVs) based on their object detection performance. The framework takes two sets of predictions from different algorithms and associates their inconsistencies with the collision risk via fuzzy inference. The first set of predictions is obtained by retrieving safety-critical 2.5D objects from a depth map, and the second set comes from the ordinary AV's 3D object detector. We experimentally validate that, based on Intersection-over-Union (IoU) and a depth discrepancy measure, the inconsistencies between the two sets of predictions strongly correlate to the error of the 3D object detector against ground truths. This correlation allows us to construct a fuzzy inference system and map the inconsistency measures to an AV collision risk indicator. In particular, we optimize the fuzzy inference system towards an existing offline metric that matches AV collision rates well. Lastly, we validate our monitor's capability to produce relevant risk estimates with the large-scale nuScenes dataset and demonstrate that it can safeguard an AV in closed-loop simulations.

Problem

• Estimates collision risk for autonomous vehicles
• Uses fuzzy inference for risk assessment
• Validates with large-scale dataset and simulations

Innovation

• Fuzzy inference for risk estimation
• Depth-aware 2.5D object detection
• Collision risk indicator optimization