DA-MCTS: Deadline-Aware Action Exploration for Safe Real-Time Planning

Abstract

Real-time motion planning for autonomous racing must operate under strict computational deadlines while maintaining safe and reliable behavior. However, the quality and safety of planning algorithms depend heavily on the computational budget available on the deployment hardware. Policies tuned on powerful systems can become unsafe on resource-constrained platforms, where limited planning iterations leave aggressive actions insufficiently explored. While existing planning approaches may limit the size of their search space, they do not explicitly adapt the risk profile of sampled actions to the available relative deadline. These approaches overlook how limited computational resources leave aggressive actions insufficiently explored, thereby compromising safety. We present Deadline-Aware MCTS (DA-MCTS), which adapts action exploration in continuous-space MCTS according to the available relative deadline. Our approach partitions the action space into conservative, aggressive, and Gaussian prior sampling categories, and uses a neural network that maps runtime features to a probability distribution over these categories. This enables safe behavior under tight budgets, where sampling shifts toward conservative actions, and improved performance as additional compute becomes available. We further extend DA-MCTS with a friction-aware feature representation that allows the planner to adapt its behavior and generalize to previously unseen friction conditions. In F1TENTH racing simulations with varying maps, friction conditions, and deadlines, we demonstrate that DA-MCTS drives conservatively and collisionfree across a wide range of friction conditions under tight deadlines while achieving progressively faster lap times as deadlines increase. We further validate these results with F1TENTH hardware-in-the-loop experiments. Source code is available at https://github.com/scope-lab-vu/DA_MCTS_RTCSA_2026