A Modeled Approach for Online Adversarial Test of Operational Vehicle Safety (extended version)

TL;DR

This paper introduces a model-driven online adversarial testing framework for operational vehicle safety, improving the generation of safety-critical scenarios by using feedback control and hierarchical modeling to better evaluate autonomous vehicle robustness.

Contribution

It presents a novel hierarchical, model-based approach for online adversarial scenario generation that ensures safety-critical conditions while respecting traffic rules.

Findings

Effective in generating safety-critical scenarios in simulations.

Applicable to both autonomous and human-driven vehicles.

Improves testing efficiency over traditional methods.

Abstract

The scenario-based testing of operational vehicle safety presents a set of principal other vehicle (POV) trajectories that seek to force the subject vehicle (SV) into a certain safety-critical situation. Current scenarios are mostly (i) statistics-driven: inspired by human driver crash data, (ii) deterministic: POV trajectories are pre-determined and are independent of SV responses, and (iii) overly simplified: defined over a finite set of actions performed at the abstracted motion planning level. Such scenario-based testing (i) lacks severity guarantees, (ii) has predefined maneuvers making it easy for an SV with intelligent driving policies to game the test, and (iii) is inefficient in producing safety-critical instances with limited and expensive testing effort. We propose a model-driven online feedback control policy for multiple POVs which propagates efficient adversarial trajectories while respecting traffic rules and other concerns formulated as an admissible state-action space. The approach is formulated in an anchor-template hierarchy structure, with the template model planning inducing a theoretical SV capturing guarantee under standard assumptions. The planned adversarial trajectory is then tracked by a lower-level controller applied to the full-system or the anchor model. The effectiveness of the methodology is illustrated through various simulated examples with the SV controlled by either parameterized self-driving policies or human drivers.