Efficient POMDP Behavior Planning for Autonomous Driving in Dense Urban Environments using Multi-Step Occupancy Grid Maps
Author:
Affiliation:
1. ZF Friedrichshafen AG,Friedrichshafen,Germany
2. Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9921415/9921739/09922353.pdf?arnumber=9922353
Reference36 articles.
1. Decision Making Under Uncertainty
2. Towards tactical behaviour planning under uncertainties for automated vehicles in urban scenarios
3. LeTS-Drive: Driving in a Crowd by Learning from Tree Search
4. Belief state planning for autonomously navigating urban intersections
5. Situation-aware decision making for autonomous driving on urban road using online POMDP
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2. Navigating Complexity: A Deep Learning Approach to Lane Change Decisions in Autonomous Vehicles;2024 5th International Conference on Computer Engineering and Application (ICCEA);2024-04-12
3. Human-Like Decision-Making of Autonomous Vehicles in Dynamic Traffic Scenarios;IEEE/CAA Journal of Automatica Sinica;2023-10
4. Real-Time, Secure, and Computationally Efficient Navigation: A Prediction-Planning Framework Using Occupancy Grid Map;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24
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