Uatc, LLC

Gm Global Technolgoy Operations, Inc.

Carnegie Mellon University

Tianyu Gu

David McAllister Bradley

Michael Lee Phillips

Chenggang Liu

Moslem Kazemi

Upali Priyantha Mudalige

Jin-Woo Lee

John M Dolan

Generally, the present disclosure is directed to systems and methods that include or otherwise leverage an iterative solver as part of optimizing a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can include a warm start generator configured to determine an initial trajectory that respects the dynamics of the autonomous vehicle and that closely tracks a speed profile determined by a constraint solver and one or more nominal paths determined by a route selector. A decision validator can analyze speed profiles and nominal paths to identify potential inconsistencies and validate a decision before optimization and execution. An initial trajectory can be further optimized by an iterative solver to determine an optimized trajectory for execution as a motion plan for the autonomous vehicle.

Initial trajectory generator for motion planning system of autonomous vehicles

A method for automated lane centering and/or lane changing purposes for a vehicle traveling on a roadway that employs roadway points from a map database to determine a reference vehicle path and sensors on the vehicle for detecting static and moving objects to adjust the reference path. The method includes reducing the curvature of the reference path to generate a reduced curvature reference path that reduces the turning requirements of the vehicle and setting the speed of the vehicle from posted roadway speeds from the map database. The method also includes providing multiple candidate vehicle paths and vehicle speeds to avoid the static and moving objects in front of the vehicle.

Unified motion planning algorithm for autonomous driving vehicle in obstacle avoidance maneuver

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Pittsburgh, PA, United States of America

Tianyu Gu