Optimization-Based Trajectory Planning With Behavior Cells for Autonomous Driving

Engineering notes

Key topics: autonomous driving, trajectory planning, on-road, planning. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Safe and executable trajectory planning in urban environments requires jointly considering traffic-related elements, ego behavior, and vehicle kinematics, posing challenges to the real-time performance and convergence of optimization-based methods. This paper proposes a behavior-guided optimization framework that structures the solution space at the spatiotemporal drivable domain level to facilitate fast and stable convergence. The planning space is partitioned into modular spatiotemporal domains, termed Behavior Cells (BCs), which encode ego motion feasibility and traffic-induced decisions. Feasible high-level behaviors are systematically enumerated through structured BC combinations and evaluated via a finite-horizon Markov decision process. The selected BC combination defines a continuous, behavior-consistent solution space, within which a dynamic two-stage optimization progressively restores the full planning formulation, enabling efficient and robust trajectory generation. Extensive simulations across diverse traffic scenarios demonstrate consistent reliability and real-time performance under varying traffic densities. On-road experiments further validate effectiveness in real-world urban environments. More detailed results are available at: https://lshasd123.github.io/Behavior-Cells/

Links and sources

• Official / arXiv page

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