PCTP: Pivot-Centric Trajectory Prediction for Long-Horizon Autonomous Driving

A recent paper published on arXiv (2608.03521) presents Pivot-Centric Trajectory Prediction (PCTP), a novel approach aimed at enhancing long-term motion forecasting for self-driving vehicles. This cross-type submission tackles the difficulty of accurately predicting the future movements of nearby agents over extended periods. Current methods, including endpoint-completion and iterative-refinement techniques, often experience weak guidance and increasing errors with longer prediction horizons. PCTP addresses these issues by introducing 'pivots'—critical points along predicted paths—and breaking the long-term forecasting task into shorter sub-tasks at different scales. The method separates trajectory prediction into pivot prediction and refinement, utilizing global map context and agent interactions for pivot identification, while local map details are used for trajectory refinement. This strategy seeks to enhance guidance and minimize error accumulation, potentially increasing the dependability of autonomous driving systems. The full paper can be accessed at https://arxiv.org/abs/2608.03521.