Dynamic Programming and Gradient Descent Based Decision and Planning for Autonomous Vehicles

Abstract

According to some embodiments, the system calculates the first trajectory based on map and route information. The system generates a path profile based on the first trajectory, the traffic rules, and obstacle information describing the one or more obstacles perceived by the ADV, wherein, for each obstacle in the plurality of obstacles, the path profile includes yielding or Decision to skim to the left or right of the obstacle. The system generates a speed profile according to traffic rules based on the route profile. The system performs a gradient descent optimization based on the path profile and the velocity profile to generate a second trajectory representing the optimized first trajectory, and controls the ADV according to the second trajectory.

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 Dynamic Programming (DP) and Gradient Descent based decision-making and planning for autonomous vehicles (ADV). Background technique

[0002] Vehicles operating in an autonomous mode (e.g., driverless) can free passengers, and 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. However, motion planning and control can be open-ended and can be difficult to optimize without some initial constraints. Furthermore, motion planning and control is applied in some cases to all types of vehicles which may be imprecise...

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