mlsanityData to 14 Oct 2026

Monte-Carlo Tree Search with Neural Network Guidance for Lane-Free Autonomous Driving

arXiv 2601.09353 ↗First posted 14 Jan 2026cs.AIcited by 0

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 9 of its 32 references that are in the map. First posted on arXiv 14 Jan 2026.

Builds on

9 of 32 references.

References · 9

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
  2. 2014cuDNN: Efficient Primitives for Deep Learningcited by 245
  3. 2021Monte Carlo Tree Search: A Review of Recent Modifications and Applicationscited by 161
  4. 2020Confidence-Aware Learning for Deep Neural Networkscited by 113
  5. 2023Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applicationscited by 16
  6. 2019Lane-free Artificial-Fluid Concept for Vehicular Trafficcited by 2
  7. 2021Two-Dimensional Cruise Control of Autonomous Vehicles on Lane-Free Roadscited by 2
  8. 2022A general approach for running Python codes in OpenFOAM using an embedded pybind11 Python interpretercited by 2
  9. 2022Optimal Trajectory Planning for Connected and Automated Vehicles in Lane-free Traffic with Vehicle Nudgingcited by 2

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  2. 2024Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach
  3. 2025Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks
  4. 2022Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning
  5. 2021Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
  6. 2024Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving
  7. 2019Driving in Dense Traffic with Model-Free Reinforcement Learning
  8. 2026DriveAnchor: Progressive Anchor-based Flow Learning for Autonomous Driving Planning