Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies

Abstract

Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general methodology for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensions, the proposed methodology is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed methods can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.

arXiv comment12 pages, 13 figures

References in corpus (5)

• End to End Learning for Self-Driving Cars
• Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques
• Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
• Query-Efficient Imitation Learning for End-to-End Autonomous Driving
• Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework

Cited by in corpus (13)

• Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
• Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
• Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework
• Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review
• 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
• Specification-Guided Critical Scenario Identification for Automated Driving
• Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
• Real-world Troublemaker: A 5G Cloud-controlled Track Testing Framework for Automated Driving Systems in Safety-critical Interaction Scenarios
• Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles
• A Learning-based Stochastic Driving Model for Autonomous Vehicle Testing