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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.09353 |
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| _version_ | 1866918288884236288 |
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| author | Peridis, Ioannis Troullinos, Dimitrios Chalkiadakis, Georgios Giankoulidis, Pantelis Papamichail, Ioannis Papageorgiou, Markos |
| author_facet | Peridis, Ioannis Troullinos, Dimitrios Chalkiadakis, Georgios Giankoulidis, Pantelis Papamichail, Ioannis Papageorgiou, Markos |
| contents | Lane-free traffic environments allow vehicles to better harness the lateral capacity of the road without being restricted to lane-keeping, thereby increasing the traffic flow rates. As such, we have a distinct and more challenging setting for autonomous driving. In this work, we consider a Monte-Carlo Tree Search (MCTS) planning approach for single-agent autonomous driving in lane-free traffic, where the associated Markov Decision Process we formulate is influenced from existing approaches tied to reinforcement learning frameworks. In addition, MCTS is equipped with a pre-trained neural network (NN) that guides the selection phase. This procedure incorporates the predictive capabilities of NNs for a more informed tree search process under computational constraints. In our experimental evaluation, we consider metrics that address both safety (through collision rates) and efficacy (through measured speed). Then, we examine: (a) the influence of isotropic state information for vehicles in a lane-free environment, resulting in nudging behaviour--vehicles' policy reacts due to the presence of faster tailing ones, (b) the acceleration of performance for the NN-guided variant of MCTS, and (c) the trade-off between computational resources and solution quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09353 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Monte-Carlo Tree Search with Neural Network Guidance for Lane-Free Autonomous Driving Peridis, Ioannis Troullinos, Dimitrios Chalkiadakis, Georgios Giankoulidis, Pantelis Papamichail, Ioannis Papageorgiou, Markos Artificial Intelligence Lane-free traffic environments allow vehicles to better harness the lateral capacity of the road without being restricted to lane-keeping, thereby increasing the traffic flow rates. As such, we have a distinct and more challenging setting for autonomous driving. In this work, we consider a Monte-Carlo Tree Search (MCTS) planning approach for single-agent autonomous driving in lane-free traffic, where the associated Markov Decision Process we formulate is influenced from existing approaches tied to reinforcement learning frameworks. In addition, MCTS is equipped with a pre-trained neural network (NN) that guides the selection phase. This procedure incorporates the predictive capabilities of NNs for a more informed tree search process under computational constraints. In our experimental evaluation, we consider metrics that address both safety (through collision rates) and efficacy (through measured speed). Then, we examine: (a) the influence of isotropic state information for vehicles in a lane-free environment, resulting in nudging behaviour--vehicles' policy reacts due to the presence of faster tailing ones, (b) the acceleration of performance for the NN-guided variant of MCTS, and (c) the trade-off between computational resources and solution quality. |
| title | Monte-Carlo Tree Search with Neural Network Guidance for Lane-Free Autonomous Driving |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.09353 |