Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles

Understand

This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving.

• The decision making and trajectory planning tasks are jointly formulated as a nonlinear programming problem with an integrated objective function.
• However, integrating the discrete decision variables into the continuous trajectory optimization leads to a mixed-integer programming (MIP) problem with inherent nonlinearity and nonconvexity.
• To address the challenge in solving the problem, the original problem is decomposed into two sub-stages, and a two-stage optimization (TSO) based approach is presented to ensure the coherence in outcomes for the two stages.