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

Abstract

DOI: 10.2139/ssrn.5314027

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 features of test scenarios, and their inter-dependencies, influence AV test scenario outcomes.

Method

This study proposes an integrated approach using Instance Space Analysis (ISA) to evaluate both types of features, identify key influences on AV safety, and predict test outcomes without execution.

Results

Our study identifies critical features affecting test outcomes (effective or 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) cost-effective test outcome prediction for optimized regression testing.