ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving
Author:
Affiliation:
1. University of Chinese Academy of Sciences,Beijing,China
2. Royal Institute of Technology (KTH),Dept. of Engineering Design,Stockholm,Sweden
3. Institute of Software Chinese Academy of Sciences,Beijing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10317938/10317940/10317968.pdf?arnumber=10317968
Reference21 articles.
1. Experience Paper: Search-Based Testing in Automated Driving Control Applications
2. Causalaf: Causal autore-gressive flow for safety-critical driving scenario generation;ding;6th Annual Conference on Robot Learning,0
3. GeoScenario: An Open DSL for Autonomous Driving Scenario Representation
4. Paracosm: A Test Framework for Autonomous Driving Simulations
5. Attention is all you need;vaswani;Advances in neural information processing systems,2017
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1. MACS: Multi-Agent Adversarial Reinforcement Learning for Finding Diverse Critical Driving Scenarios;2024 IEEE Conference on Software Testing, Verification and Validation (ICST);2024-05-27
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