Abstract
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.
Original language | English |
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Title of host publication | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1399-1404 |
Number of pages | 6 |
ISBN (Electronic) | 9781538670248 |
DOIs | |
Publication status | Published - Oct 2019 |
Event | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019 |
Publication series
Name | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
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Conference
Conference | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
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Country/Territory | New Zealand |
City | Auckland |
Period | 27/10/19 → 30/10/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.