TY - JOUR
T1 - A Two-Stage Optimization-Based Motion Planner for Safe Urban Driving
AU - Eiras, Francisco
AU - Hawasly, Majd
AU - Albrecht, Stefano V.
AU - Ramamoorthy, Subramanian
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
KW - Autonomous urban driving
KW - model predictive control (MPC)
KW - motion planning
KW - optimization
UR - http://www.scopus.com/inward/record.url?scp=85125679368&partnerID=8YFLogxK
U2 - 10.1109/TRO.2021.3088009
DO - 10.1109/TRO.2021.3088009
M3 - Article
AN - SCOPUS:85125679368
SN - 1552-3098
VL - 38
SP - 822
EP - 834
JO - IEEE Transactions on Robotics
JF - IEEE Transactions on Robotics
IS - 2
ER -