Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles, Part I: Methodology

2026-10-06

AI Summary

This study addresses the limitation of traditional functional safety in evaluating the interactive behaviors between autonomous vehicles and traffic environments by proposing a novel "behavioral safety" paradigm, shifting the focus from vehicle-centric to interaction-centric assessment. Methodologically, it constructs a third-party evaluation framework encompassing both capability and intelligence testing, employing a hybrid approach that integrates controlled-scenario reaction tests with naturalistic traffic interaction tests to quantify safety metrics under real-world road conditions. This project establishes systematic behavioral safety evaluation standards prior to large-scale deployment and provides statistically rigorous safety measurement methods, thereby laying a foundation for the subsequent verification of open-source systems.

Abstract

Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional functional safety approaches, which primarily verify the reliability, robustness, and adequacy of AV hardware and software systems from a vehicle-centric perspective, do not sufficiently address the AV's broader interactions and behavioral impact on the surrounding traffic environment. To overcome this limitation, we propose a paradigm shift toward behavioral safety, a comprehensive approach focused on evaluating AV responses and interactions within the traffic environment. To systematically assess behavioral safety, we introduce a third-party AV safety assessment framework comprising two complementary evaluation components: the Behavioral Competency Test and the Driving Intelligence Test. The Behavioral Competency Test evaluates the AV's reactive behaviors under controlled scenarios, ensuring basic behavioral competency. In contrast, the Driving Intelligence Test assesses the AV's interactive behaviors within naturalistic traffic conditions, quantifying the frequency of safety-critical events to deliver statistically meaningful safety metrics before large-scale deployment. In Part II of this study, an open-source Level 4 Automated Driving System (ADS) is tested to demonstrate the effectiveness of the proposed method.

Problem

• Autonomous Vehicles
• Behavioral Safety
• Safety Assessment
• Large-scale Deployment
• Traffic Interaction

Innovation

• Behavioral Safety
• Autonomous Vehicles
• Safety Assessment Framework
• Driving Intelligence Test
• Behavioral Competency Test