Coverage-Guided Road Selection and Prioritization for Efficient Testing in Autonomous Driving Systems

TL;DR

This paper introduces a novel test prioritization framework for autonomous driving systems that reduces redundant road scenario testing by clustering based on diversity, prioritizing critical cases, and significantly improving early failure detection.

Contribution

The paper presents a new clustering and prioritization method that efficiently reduces testing redundancy and enhances fault detection in autonomous driving systems.

Findings

Achieves 89% reduction in test suite size

Retains 79% of failed scenarios

Improves early failure detection by up to 95x

Abstract

Autonomous Driving Assistance Systems (ADAS) rely on extensive testing to ensure safety and reliability, yet road scenario datasets often contain redundant cases that slow down the testing process without improving fault detection. To address this issue, we present a novel test prioritization framework that reduces redundancy while preserving geometric and behavioral diversity. Road scenarios are clustered based on geometric and dynamic features of the ADAS driving behavior, from which representative cases are selected to guarantee coverage. Roads are finally prioritized based on geometric complexity, driving difficulty, and historical failures, ensuring that the most critical and challenging tests are executed first. We evaluate our framework on the OPENCAT dataset and the Udacity self-driving car simulator using two ADAS models. On average, our approach achieves an 89% reduction in test suite size while retaining an average of 79% of failed road scenarios. The prioritization strategy improves early failure detection by up to 95x compared to random baselines.