Spatial-Based Predictive Control and Geometric Corridor Planning for Adaptive Cruise Control Coupled With Obstacle Avoidance

This paper presents an integrated control approach for autonomous driving comprising a corridor path planner that determines constraints on vehicle position, and a linear time-varying model predictive controller combining path planning and tracking in a road-aligned coordinate frame. The capabilities of the approach are illustrated in obstacle-free curved road-profile tracking, in an application coupling adaptive cruise control (ACC) with obstacle avoidance (OA), and in a typical driving maneuver on highways. The vehicle is modeled as a nonlinear dynamic bicycle model with throttle, brake pedal position, and steering angle as control inputs. Proximity measurements are assumed to be available within a given range field surrounding the vehicle. The proposed general feedback control architecture includes an estimator design for fusion of database information (maps), exteroceptive as well as proprioceptive measurements, a geometric corridor planner based on graph theory for the avoidance of multiple, potentially dynamically moving objects, and a spatial-based predictive controller. Switching rules for transitioning between four different driving modes, i.e., ACC, OA, obstacle-free road tracking (RT), and controlled braking (Brake), are discussed. The proposed method is evaluated on test cases, including curved and highway two-lane road tracks with static as well as moving obstacles.

I. Introduction

Autonomous driving requires handling a plethora of different driving scenarios, ranging from parking to crash avoidance on challenging road conditions. The objective is to ensure road safety and increase passenger comfort. A suitable candidate for the control strategy is model predictive control (MPC), due to its ability to incorporate system constraints and nonlinearities in a systematic way.