Assessing the Criticality of Longitudinal Driving Scenarios using Time Series Data

2023/05/29 by Nico Schick, Schick, Nico
Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotics (cs.RO) #Traffic and Road Safety #Vehicle emissions and performance

paper · pdf · doi:10.48550/arxiv.2305.20071

openalex publication_date 2023/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Unfortunately, many people die in car accidents. To reduce these accidents, cars are equipped with driving safety systems. With autonomous vehicles, the driver's behavior becomes irrelevant as the car drives autonomously. All autonomous driving algorithms must undergo extensive testing and validation, especially for safety-critical scenarios. Therefore, the detection of safety-critical driving scenarios is essential for autonomous vehicles. This publication describes safety indicator metrics based on time series covering longitudinal driving data to detect safety-critical driving scenarios.

Related

• A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective
• Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
• Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
• Synthesizing Follow-Up Drive Data for Enhanced Road Safety in Intelligent Driving Function Systems
• Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review