Counterfactual regret minimization for the safety verification of autonomous driving
- 14 January 2025
- journal article
- research article
- Published by Springer Nature in Applied Intelligence
- Vol. 55 (5) , 1-13
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (52131201)
This publication has 22 references indexed in Scilit:
- A Survey on an Emerging Safety Challenge for Autonomous Vehicles: Safety of the Intended FunctionalityEngineering, 2024
- Data generation for connected and automated vehicle tests using deep learning modelsAccident Analysis & Prevention, 2023
- RM-FSP: Regret minimization optimizes neural fictitious self-playNeurocomputing, 2023
- Controllable probability-limited and learning-based human-like vehicle behavior and trajectory generation for autonomous driving testing in highway scenarioExpert Systems with Applications, 2023
- Deep Adversarial Reinforcement Learning based Incentive Mechanism for Content Delivery in D2D-Enabled Mobile NetworksNeurocomputing, 2023
- Deep learning-based vehicle trajectory prediction based on generative adversarial network for autonomous driving applicationsMultimedia Tools and Applications, 2022
- Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing TestAdvanced Intelligent Systems, 2022
- Factors affecting lane change crashesIATSS Research, 2020
- Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?Transportation Research Part A: Policy and Practice, 2016
- Experience with Traffic Conflicts in Canada with Emphasis on “Post Encroachment Time” Techniques1984