Safety-Centered Scenario Generation for Autonomous Vehicles

TL;DR

This paper introduces a scenario generation framework for autonomous vehicles that creates diverse, safety-critical situations to validate safety features through simulation, supporting regulatory compliance and system robustness.

Contribution

A novel framework for generating diverse, safety-critical driving scenarios that align with safety standards and enable comprehensive validation of autonomous vehicle safety features.

Findings

Framework supports regulatory and edge case scenarios.

Provides quantitative safety metrics for evaluation.

Enables systematic testing of safety features and system limitations.

Abstract

This paper presents a scenario generation framework that creates diverse, parametrized, and safety-critical driving situations to validate the safety features of autonomous vehicles in simulation [15]. By modeling factors such as road geometry, traffic participants, environmental conditions, and perception uncertainties, the framework enables repeatable and scalable testing of safety mechanisms, including emergency braking, evasive maneuvers, and vulnerable road user protection. The framework supports both regulatory and edge case scenarios, mapped to hazards and safety goals derived from Hazard Analysis and Risk Assessment (HARA), ensuring traceability to ISO 26262 functional safety requirements and performance limitations. The output from these simulations provides quantitative safety metrics such as time-to-collision, minimum distance, braking and steering performance, and residual collision severity. These metrics enable the systematic evaluation of evasive maneuvering as a safety feature, while highlighting system limitations and edge-case vulnerabilities. Integration of scenario-based simulation with safety engineering principles offers accelerated validation cycles, improved test coverage at reduced cost, and stronger evidence for regulatory and stakeholder confidence.