A Two-Stage Optimization-Based Motion Planner for Safe Urban Driving

Francisco Eiras*, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to pose safety requirements as planning constraints in nonlinear, nonconvex optimization problems of motion synthesis. However, many implementations of this approach are limited by uncertain convergence and local optimality of the solutions achieved, affecting overall robustness. To improve upon these issues, we propose a novel two-stage optimization framework: In the first stage, we find a solution to a mixed-integer linear programming (MILP) formulation of the motion synthesis problem, the output of which initializes a second nonlinear programming (NLP) stage. The MILP stage enforces hard constraints of safety and road rule compliance generating a solution in the right subspace, while the NLP stage refines the solution within the safety bounds for feasibility and smoothness. We demonstrate the effectiveness of our framework via simulated experiments of complex urban driving scenarios, outperforming a state-of-the-art baseline in metrics of convergence, comfort, and progress.

Original languageEnglish
Pages (from-to)822-834
Number of pages13
JournalIEEE Transactions on Robotics
Volume38
Issue number2
DOIs
Publication statusPublished - 1 Apr 2022
Externally publishedYes

Keywords

  • Autonomous urban driving
  • model predictive control (MPC)
  • motion planning
  • optimization

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