FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality

TL;DR

FREA is a new method for generating safety-critical autonomous vehicle scenarios that balances adversariality with feasibility by leveraging the Largest Feasible Region, improving robustness and realism in testing.

Contribution

FREA introduces a feasibility-guided approach using the Largest Feasible Region to generate realistic, safety-critical scenarios for autonomous vehicle testing.

Findings

Effectively generates safety-critical scenarios with near-miss events.

Ensures scenarios are feasible for autonomous vehicles.

Demonstrates robustness across different AV models and environments.

Abstract

Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Existing methods focus on optimizing adversariality while preserving the naturalness of scenarios, aiming to achieve a balance through data-driven approaches. However, without an appropriate upper bound for adversariality, the scenarios might exhibit excessive adversariality, potentially leading to unavoidable collisions. In this paper, we introduce FREA, a novel safety-critical scenarios generation method that incorporates the Largest Feasible Region (LFR) of AV as guidance to ensure the reasonableness of the adversarial scenarios. Concretely, FREA initially pre-calculates the LFR of AV from offline datasets. Subsequently, it learns a reasonable adversarial policy that controls the scene's critical background vehicles (CBVs) to generate adversarial yet AV-feasible scenarios by maximizing a novel feasibility-dependent adversarial objective function. Extensive experiments illustrate that FREA can effectively generate safety-critical scenarios, yielding considerable near-miss events while ensuring AV's feasibility. Generalization analysis also confirms the robustness of FREA in AV testing across various surrogate AV methods and traffic environments.