Research papers · Posted 2021.10.16 · Preprint

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Xinhai Zhang, Jianbo Tao, Kaige Tan, Martin Törngren, José Manuel Gaspar Sánchez, Muhammad Rusyadi Ramli, Xin Tao, Magnus Gyllenhammar, Franz Wotawa, Naveen Mohan, Mihai Nica, Hermann Felbinger · arXiv · English

Overview

This systematic review classifies critical-scenario identification by problem definition, generation method, and scenario assessment. It synthesizes research approaches and discusses unresolved issues in coverage, practical applicability, and scenario-space explosion.

Author-supplied source information

Author-supplied information on arXiv.

37 pages, 24 figures

Version: v1

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Overview

Author-supplied source information

Participation enquiry

Your enquiry has been sent.

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Overview

Author-supplied source information

Related resources

Assuring Learning-Enabled Cyber-Physical Systems with Foundation Models: A Neuro-Symbolic Agenda for Testing, Verification, and Runtime Assurance

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

A Deployed Hybrid Vehicle-in-the-Loop Platform for Validating Cooperative Perception