- Abstract: Guaranteeing safety in motion planning is a crucial bottleneck on the path towards wider adoption of autonomous driving technology. A promising direction is to pose safety requirements as planning constraints in nonlinear optimization problems of motion synthesis. However, many implementations of this approach are hindered by uncertain convergence and local optimality of the solutions, affecting the planner’s overall robustness. In this paper, we propose a novel two-stage optimization framework: we first find the solution to a Mixed-Integer Linear Programming (MILP) approximation of the motion synthesis problem, which in turn initializes a second Nonlinear Programming (NLP) formulation. We show that initializing the NLP stage with the MILP solution leads to better convergence, lower costs, and outperforms a state-of-the-art Nonlinear Model Predictive Control baseline in both progress and comfort metrics.
- TL;DR: This paper introduces an optimization framework for autonomous driving which first solves a linearized version of the motion planning problem to initialize a second nonlinear optimization stage, leading to better overall performance.
- Keywords: Motion Planning, Trajectory Optimization, Autonomous Driving, Optimization, Model Predictive Control