Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework

Abstract

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this paper, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a new-designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study was performed and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.

arXiv comment10 pages, 10 figures

References in corpus (5)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques
• A Tutorial on Bayesian Optimization
• Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
• Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies

Cited by in corpus (10)

• Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
• Towards Guaranteed Safety Assurance of Automated Driving Systems with Scenario Sampling: An Invariant Set Perspective (Extended Version)
• Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review
• Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing
• Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates
• Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios
• Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
• Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles
• A Learning-based Stochastic Driving Model for Autonomous Vehicle Testing
• An Interaction-aware Evaluation Method for Highly Automated Vehicles