A Hybrid Tactical Decision-Making Approach in Automated Driving Combining Knowledge-Based Systems and Reinforcement Learning
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Decision-making in automated driving is influenced both by objective traffic rules and subjective perceptions and goals of the driver. Thus, a suitable representation of the environment of the autonomous vehicle is required to model complex traffic situations and extract key features. To achieve this objective, this work uses an ontology-based situation interpretation (OBSI) to model traffic situations. The resulting semantic state representation is used to train models of vehicle-controlling agents using reinforcement learning. Based on our simulations, it can be shown that the semantic preprocessing of traffic situations significantly improves the agent's performance regarding safety and driving style.
Details
Original language | English |
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Title of host publication | 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) |
Pages | 3478-3483 |
Number of pages | 6 |
ISBN (electronic) | 9781665468800 |
Publication status | Published - 8 Oct 2022 |
Peer-reviewed | Yes |
External IDs
Scopus | 85141869549 |
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