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

2026-03-22

AI Summary

This work addresses the challenge of generating safety-critical scenarios in autonomous driving simulation testing, where the intricate coupling between static road features and dynamic vehicle behaviors remains inadequately modeled. To this end, the paper proposes the first unified safety evaluation framework that jointly integrates both static and dynamic characteristics. Leveraging Instance Space Analysis (ISA), the approach identifies key factors influencing test outcomes by co-modeling these heterogeneous features and constructs a machine learning model capable of predicting test results without actual execution. Experimental evaluations demonstrate that the proposed method significantly improves prediction accuracy compared to baseline models relying solely on either static or dynamic features alone, thereby enhancing the efficiency of regression testing in autonomous driving validation.

Technology Category

• Machine Learning: Evaluation and Analysis
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• autonomous vehicles
• safety testing
• road features
• vehicle dynamics
• test scenario

Innovation

• Instance Space Analysis
• Autonomous Vehicles
• Static and Dynamic Features
• Safety Testing
• Test Outcome Prediction