Addressing the IEEE AV Test Challenge with Scenic and VerifAI

TL;DR

This paper presents a systematic simulation-based testing framework for autonomous vehicles using Scenic for scenario modeling and VerifAI for failure detection, effectively identifying failure cases in open-source autopilot systems.

Contribution

It introduces a formal, probabilistic scenario generation and search methodology for AV testing, combining Scenic and VerifAI to improve failure detection in simulation.

Findings

Successfully identified failure scenarios in Apollo autopilot

Demonstrated effectiveness in realistic traffic scenarios

Provided a systematic approach for AV testing

Abstract

This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveraging our previous work on formally-driven simulation for intelligent cyber-physical systems. First, to model and generate interactive scenarios involving multiple agents, we used Scenic, a probabilistic programming language for specifying scenarios. A Scenic program defines an abstract scenario as a distribution over configurations of physical objects and their behaviors over time. Sampling from an abstract scenario yields many different concrete scenarios which can be run as test cases for the AV. Starting from a Scenic program encoding an abstract driving scenario, we can use the VerifAI toolkit to search within the scenario for failure cases with respect to multiple AV evaluation metrics. We demonstrate the effectiveness of our testing framework by identifying concrete failure scenarios for an open-source autopilot, Apollo, starting from a variety of realistic traffic scenarios.