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Path planning is an essential function in an intelligent vehicle, especially when driving in scenarios cluttered by large-scale static obstacles. Traditional path planners often struggle to find a balance among speed, accuracy, and optimality in their solutions. In this paper, we introduce an Adaptive Pure Pursuit (APP) planner, which is designed to be fast and near-optimal for autonomous driving in cluttered environments. The APP planner generates feasible paths through a simulated closed-loop tracking control process of a virtual vehicle. If a derived path encounters obstacles, an adaptive refinement step is taken to locally reduce these collisions. Unlike search-based planners that suffer from the "curse of dimensionality" and optimization-based methods that often run slowly, the APP planner operates extremely fast. The high speed stems from the fact that both the virtual controller simulation and the refinement step involve computations with zero degrees of freedom. The proposed APP planner outperforms the prevalent optimizationbased and search-based path planners, as shown by comparative simulations. Real-world experiments were also conducted to validate the APP planner, and its source codes are provided at https://github.com/libai1943/Adaptive_Pure_Pursuit_Planner.

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