Optimization-Based Trajectory Planning With Behavior Cells for Autonomous Driving

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.