Non-linear reference line optimization method using piecewise quintic polynomial spiral paths for operating autonomous driving vehicles

Abstract

A first reference line representing a trajectory from a first location to a second location associated with an autonomous driving vehicle (ADV) is received. The first reference line is segmented intoa number of reference line segments. For each of the reference line segments, a quintic polynomial function is defined to represent the reference line segment. An objective function is determined based on the quintic polynomial functions of the reference line segments. An optimization is performed on coefficients of the quintic polynomial functions in view of a set of constraints associated with the reference line segments, such that an output of the objective function reaches minimum while the constraints are satisfied. A second reference line is then generated based on the optimized parameters or coefficients of the quintic polynomial functions of the objective function. The second reference line is then utilized to plan and control the ADV.

Description

technical field [0001] Embodiments of the present disclosure generally relate to operating an autonomous vehicle. More specifically, embodiments of the present disclosure relate to generating a reference route for operating a trajectory of an autonomous vehicle. Background technique [0002] Vehicles operating in an autonomous mode (e.g., driverless) can free passengers (especially the driver) from some driving-related duties. When operating in autonomous mode, the vehicle can navigate to various locations using on-board sensors, allowing the vehicle to drive with minimal human interaction or in some cases without any passengers. [0003] Motion planning and control are key operations in autonomous driving. Specifically, trajectory planning is a key component in autonomous driving systems. Traditional trajectory planning techniques rely heavily on a high-quality reference line (as a guiding path (e.g., the centerline of a road) for an autonomous vehicle) to generate stabl...

AI Technical Summary

Problems solved by technology

Often, map data (usually a series of two-dimensional (2D) points in a world coordinate system) directly from sensors does not provide the required smoothness, and thus, using map data directly may cause the planner to lag between planning cycles. produce unstable and oscillating trajectories

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