Waymo LLC

Carl Kershaw

David Harrison Silver

Pankaj Chaudhari

Abhijit S Ogale

Chen Wu

Congcong Li

Henrik Kretzschmar

Victoria Dean

Edward Hsiao

Jonathan Hsiao

Aspects of the disclosure relate to detecting and responding to malfunctioning traffic signals for a vehicle having an autonomous driving mode. For instance, information identifying a detected state of a traffic signal for an intersection. An anomaly for the traffic signal may be detected based on the detected state and prestored information about expected states of the traffic signal. The vehicle may be controlled in the autonomous driving mode based on the detected anomaly.

Detecting and responding to malfunctioning traffic signals for autonomous vehicles

Aspects of the disclosure provide for a method for identifying speed limit signs and controlling an autonomous vehicle in response to detected speed limit signs. The autonomous vehicle's computing devices identifies a speed limit sign in a vehicle's environment and a location and orientation corresponding to the speed limit sign. Then, the and orientation location of the speed limit sign is determined to not correspond to a pre-stored location and a pre-stored orientation of a speed limit sign that is pre-stored in map information. An effect zone of the speed limit sign is determined based on the location and orientation of the speed limit sign and characteristics of surrounding areas or other detected object before or after the speed limit sign. The autonomous vehicle's computing devices determines a response of the vehicle based on the determined effect zone, and controls the autonomous vehicle based on the determined response.

Autonomous vehicle system configured to respond to temporary speed limit signs

Aspects of the disclosure relate to training and using a phrase recognition model to identify phrases in images. As an example, a selected phrase list may include a plurality of phrases is received. Each phrase of the plurality of phrases includes text. An initial plurality of images may be received. A training image set may be selected from the initial plurality of images by identifying the phrase-containing images that include one or more phrases from the selected phrase list. Each given phrase-containing image of the training image set may be labeled with information identifying the one or more phrases from the selected phrase list included in the given phrase-containing images. The model may be trained based on the training image set such that the model is configured to, in response to receiving an input image, output data indicating whether a phrase of the plurality of phrases is included in the input image.

Phrase recognition model for autonomous vehicles

The technology relates to detecting a person directing traffic. For instance, a first vehicle may be controlled in an autonomous driving mode. Sensor data is received from a perception system of the first vehicle, the sensor data identifying a person. Behavior of the person is observed using the sensor data. The observed behavior of the person and the observed behavior of a second vehicle are used to determine a likelihood value indicative of whether the person is directing traffic. The first vehicle is maneuvered in the autonomous driving mode based on the determined likelihood value.

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San Francisco, CA, United States of America

Carl Kershaw