V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach

Researchers Pengfei Liu, Jialing Zhou, Yuezu Lv, Guanghui Wen, and Tingwen Huang have introduced V-STC (variable-time-step spatio-temporal corridor), a new optimization-based approach for coordinating multiple autonomous vehicles (AVs). The method addresses a core trade-off in multi-vehicle trajectory planning: ensuring collision-free motion while minimizing time spent in shared space. Traditional spatio-temporal corridor (STC) methods use fixed time steps, which can lead to inefficient occupancy. V-STC breaks this constraint by treating both the spatial position of corridor cubes and their time durations as decision variables in an optimization model. This allows each vehicle's corridor to adapt dynamically, reducing overall temporal occupancy while maintaining safety.

In simulation studies, V-STC consistently outperformed existing STC approaches, achieving more time-efficient motion across various multi-vehicle scenarios. The algorithm first constructs a V-STC for each AV via optimization, then independently plans a dynamically feasible trajectory within that corridor. The paper, submitted to arXiv on April 24, 2026, spans 12 pages with 23 figures and is categorized under Robotics and Multiagent Systems. This work is particularly relevant for autonomous driving fleets, warehouse robots, and drone swarms where space and time are at a premium. By enabling tighter coordination without sacrificing safety, V-STC could help scale autonomous operations in logistics, transportation, and smart cities.