The Role of Road Features and Vehicle Dynamics in Cost-Effective Autonomous Vehicles Safety Testing: Insights from Instance Space Analysis

TL;DR

This paper presents an integrated approach using Instance Space Analysis to evaluate how static road features and dynamic vehicle behaviors influence autonomous vehicle safety testing, improving prediction accuracy and fault detection.

Contribution

It introduces a unified framework combining static and dynamic features with ISA for better AV safety assessment and outcome prediction.

Findings

Combining static and dynamic features improves prediction accuracy.

Key features significantly influence safety-critical test outcomes.

Models using both feature types outperform single-feature models.

Abstract

Context: Simulation-based testing is a cost-efficient alternative to field testing for Autonomous Vehicles (AVs), but generating safety-critical test cases is challenging due to the vast search space. Prior work has studied static (road features) and dynamic (AV behavior) features of test scenarios separately, but their inter-dependencies are underexplored. Objective: In this paper, we describe an empirical to analyze how static and dynamic featuresof test scenarios, and their inter-dependencies, influence AV test scenario outcomes. Method: This study proposes an integrated approach using Instance Space Analysis (ISA) toevaluate both types of features, identify key influences on AV safety, and predict test outcomeswithout execution. Results: Our study identifies critical features affecting test outcomes (effective/ineffective, depending on whether it leads to a safety-critical condition). Results show that combining static and dynamic features improves prediction accuracy, confirmed by models trained on both feature types outperforming models trained with only one type of feature. Conclusion: The interplay of static and dynamic features enhances fault detection in AV testing. This research underscores the importance of integrating both types of features to create more effective testing frameworks for autonomous systems. Key contributions include: (1) a unified framework for AV safety assessment, (2) identification of influential features using ISA, and (3) efficient test outcome prediction for optimized regression testing.