Waymo LLC

Xu Dong

Jonathan Lee Pedersen

Yu Zheng

Mishika Vora

Amir Takhmar

Kevin Joseph Malta

Daniella Gutlansky

Jakob Robert Zwiener

Chung Eun Kim

Eamonn Michael Doherty

Trang Uyen Thien Tran

Brian Clair Williammee

The technology involves pick-up and drop-off techniques for autonomous vehicles. Pullover selection options are evaluated by considering road user inconvenience and dynamic changes to such inconvenience. In response, a change in vehicle operation is made as needed. This process may consider whether a given situation is 'likely-inconvenient' or “unlikely-inconvenient” based on both statie and dynamic signals from the environment for a likely pick-up or drop-off location. The vehicle may request remote support to determine whether to pull over or to move from a stopped location, for instance based on changes to dynamic signals associated with other road users. In double parking situations, the vehicle may determine whether to remain double parked or move to limit inconvenience to a minimum number of other vehicles, such as by blocking as few other vehicles as possible or by making it as easy as possible for a parked vehicle to pull out.

Pick-up and drop-off signals for managing double parking obstruction

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

The technology involves pullover maneuvers for vehicles operating in an autonomous driving mode. A vehicle operating in an autonomous driving mode is able to identify parking locations that may be occluded or otherwise not readily visible. A best case estimation for any potential parking locations, including partially or fully occluded areas, is compared against predictions for identified visible parking locations. This can include calculating a cost for each potential parking location and comparing that cost to a baseline cost. Upon determining that an occluded location would provide a more viable option than any visible parking locations, the vehicle is able to initiate a driving adjustment (e.g., slow down) prior to arriving at the occluded location. This enables the vehicle to minimize passenger discomfort while also providing notice to other road users of an intent to pull over.

Speed reasoning in the presence of uncertainty when pulling over

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Mountain View, CA, United States of America

Xu Dong