Abstract
Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning problem, where weight coefficients are automatically learned along with the policy, eliminating the need for scenario-specific tuning. Empirically, our approach outperforms traditional RL in efficiency and reliability. Additionally, real-world demonstrations validate its practical value for real-world autonomous driving.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025 |
| Editors | Yevgeniy Vorobeychik, Sanmay Das, Ann Nowe |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
| Pages | 3026-3028 |
| Number of pages | 3 |
| ISBN (Electronic) | 9798400714269 |
| Publication status | Published - 2025 |
| Externally published | Yes |
| Event | 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025 - Detroit, United States Duration: May 19 2025 → May 23 2025 |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| ISSN (Print) | 1548-8403 |
| ISSN (Electronic) | 1558-2914 |
Conference
| Conference | 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025 |
|---|---|
| Country/Territory | United States |
| City | Detroit |
| Period | 5/19/25 → 5/23/25 |
Bibliographical note
Publisher Copyright:© 2025 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org).
ASJC Scopus Subject Areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
Keywords
- Autonomous Driving
- Lane Keeping
- Reinforcement Learning