Waymo LLC

Eamonn Michael Doherty

Jonathan Lee Pedersen

Salil Pandit

Bruce Mai

Eric Friedman

Kevin Joseph Malta

Daniella Gutlansky

Yu Zheng

Gaurav Agarwal

Andrew Chan

Chung Eun Kim

Brian Clair Williammee

Aspects of the disclosure relate to evaluating quality of a predetermined pullover location for an autonomous vehicle. For instance, a plurality of inputs for the predetermined pullover location may be received. The plurality of inputs may each include a value representative of a characteristic of the predetermined pullover location. The plurality of inputs may be combined to determine a pullover quality value for the predetermined pullover location. The pullover quality value may be provided to a vehicle in order to enable the vehicle to select a pullover location for the vehicle.

Quality scoring for pullovers for autonomous vehicles

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a pull-over location using machine learning. One of the methods includes obtaining data specifying a target pull-over location for an autonomous vehicle travelling on a roadway. A plurality of candidate pull-over locations in a vicinity of the target pull-over location are identified. For each candidate pull-over location, an input that includes features of the candidate pull-over location is processed using a machine learning model to generate a respective likelihood score representing a predicted likelihood that the candidate pull-over location is an optimal location. The features of the candidate pull-over location include one or more features that compare the candidate pull-over location to the target pull-over location. Using the respective likelihood scores, one of the candidate pull-over locations is selected as an actual pull-over location for the autonomous vehicle.

Pull-over location selection using machine learning

Growing community of inventors

Mountain View, CA, United States of America

Eamonn Michael Doherty