SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments

Author:

Jamgochian Arec1,Buehrle Etienne2,Fischer Johannes2,Kochenderfer Mykel J.1

Affiliation:

1. Stanford University,Stanford,CA,USA,94305

2. Karlsruhe Institute of Technology,Karlsruhe,Germany,76131

Funder

National Science Foundation

Federal Ministry for Transport, Innovation and Technology (bmvit)

Federal Ministry for Digital, Business and Enterprise (bmdw)

Austrian Research Promotion Agency (FFG)

Styrian Business Promotion Agency (SFG)

Publisher

IEEE

Reference32 articles.

1. Adversarial option-aware hierarchical imitation learning;jing;International Conference on Machine Learning (ICML),0

2. Multi-Task Hierarchical Imitation Learning for Home Automation

3. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning

4. A divergence minimization perspective on imitation learning methods;ghasemipour;Conference on Robot Learning,0

5. OpenAI gym;brockman;ArXiv,2016

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1. Test-Driven Inverse Reinforcement Learning Using Scenario-Based Testing;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. Guiding Attention in End-to-End Driving Models;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

3. Graph-Based Adversarial Imitation Learning for Predicting Human Driving Behavior;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

4. A Survey of Artificial Intelligence Approaches to Safety and Mission-Critical Systems;2024 Integrated Communications, Navigation and Surveillance Conference (ICNS);2024-04-23

5. Optimizing Urban Sustainability;Advances in Computer and Electrical Engineering;2024-03-15

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