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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.07210 |
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| _version_ | 1866914309847646208 |
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| author | Pfaffmann, Donald Klusch, Matthias Steinmetz, Marcel |
| author_facet | Pfaffmann, Donald Klusch, Matthias Steinmetz, Marcel |
| contents | We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with heuristic confidence-based vertical pruning to reduce its execution time without compromising safety of driving. Our experimental performance analysis on the CARLA-CTS2 benchmark of critical traffic scenarios with pedestrians revealed that HyPlan may navigate safer than selected relevant baselines and perform significantly faster than considered alternative online POMDP planners. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07210 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving Pfaffmann, Donald Klusch, Matthias Steinmetz, Marcel Robotics Artificial Intelligence We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with heuristic confidence-based vertical pruning to reduce its execution time without compromising safety of driving. Our experimental performance analysis on the CARLA-CTS2 benchmark of critical traffic scenarios with pedestrians revealed that HyPlan may navigate safer than selected relevant baselines and perform significantly faster than considered alternative online POMDP planners. |
| title | HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2510.07210 |