Real-Time Unified Trajectory Planning and Optimal Control for Urban Autonomous Driving Under Static and Dynamic Obstacle Constraints

TL;DR

This paper introduces a unified MPC-based approach for real-time trajectory planning and control in urban autonomous driving, effectively handling static and dynamic obstacles while ensuring feasibility and passenger comfort.

Contribution

It proposes a novel integrated MPC scheme that combines planning and control, improving over traditional separated modules in autonomous driving systems.

Findings

Effective in various urban scenarios

Guarantees feasibility with respect to obstacles and road boundaries

Ensures passenger comfort constraints

Abstract

Trajectory planning and control have historically been separated into two modules in automated driving stacks. Trajectory planning focuses on higher-level tasks like avoiding obstacles and staying on the road surface, whereas the controller tries its best to follow an ever changing reference trajectory. We argue that this separation is (1) flawed due to the mismatch between planned trajectories and what the controller can feasibly execute, and (2) unnecessary due to the flexibility of the model predictive control (MPC) paradigm. Instead, in this paper, we present a unified MPC-based trajectory planning and control scheme that guarantees feasibility with respect to road boundaries, the static and dynamic environment, and enforces passenger comfort constraints. The scheme is evaluated rigorously in a variety of scenarios focused on proving the effectiveness of the optimal control problem (OCP) design and real-time solution methods. The prototype code will be released at https://github.com/WATonomous/control.