Waymo LLC

Maya Kabkab

Chen Wu

Zhinan Xu

Woojong Koh

Yu Ouyang

Ruichi Yu

Hang Zhao

Yu-Han Chen

Yulai Shen

Congyu Gao

Sakshi Madan

Yiran Zhang

Yingwei Li

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating a distance estimate for a target object that is depicted in an image of a scene in an environment. The system obtains data specifying (i) a target portion of the image that depicts the target object detected in the image, and (ii) one or more reference portions of the image that each depict a respective reference object detected in the image. The system further obtains, for each of the one or more reference objects, a respective distance measurement for the reference object that is a measurement of a distance from the reference object to a specified location in the environment. The system processes the obtained data to generate a distance estimate for the target object that is an estimate of a distance from the target object to the specified location in the environment.

Long range distance estimation using reference objects

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating label data for one or more target objects in an environment. The system obtains first data characterizing the environment, wherein the first data includes position data characterizing a position of the target object. The system obtains second data including one or more three-dimensional (3D) frames characterizing the environment. The system determines, based on the first data, a guide feature for locating the target object in the 3D frames of the second data. The system receives a first user input that specifies at least an object position in the selected 3D frame, and generates label data for the target object based on the first user input.

Object labeling for three-dimensional data

The technology provides a sign detection and classification methodology. A unified pipeline approach incorporates generic sign detection with a robust parallel classification strategy. Sensor information such as camera imagery and lidar depth, intensity and height (elevation) information are applied to a sign detector module. This enables the system to detect the presence of a sign in a vehicle's external environment. A modular classification approach is applied to the detected sign. This includes selective application of one or more trained machine learning classifiers, as well as a text and symbol detector. Annotations help to tie the classification information together and to address any conflicts with different outputs from different classifiers. Identification of where the sign is in the vehicle's surrounding environment can provide contextual details. Identified signage can be associated with other objects in the vehicle's driving environment, which can be used to aid the vehicle in autonomous driving.

Pipeline architecture for road sign detection and evaluation

The described aspects and implementations enable efficient identification of real and image signs in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes obtaining, using a sensing system of the AV, a combined image that includes a camera image and a depth information for a region of an environment of the AV, classifying a first sign in the combined image as an image-true sign, performing a spatial validation of the first sign, which includes evaluation of a spatial relationship of the first sign and one or more objects in the region of the environment of the AV, and identifying, based on the performed spatial validation, the first sign as a real sign.

Identification of real and image sign detections in driving applications

The technology provides a sign detection and classification methodology. A unified pipeline approach incorporates generic sign detection with a robust parallel classification strategy. Sensor information such as camera imagery and lidar depth, intensity and height (elevation) information are applied to a sign detector module. This enables the system to detect the presence of a sign in a vehicle's externa environment. A modular classification approach is applied to the detected sign. This includes selective application of one or more trained machine learning classifiers, as well as a text and symbol detector. Annotations help to tie the classification information together and to address any conflicts with different the outputs from different classifiers. Identification of where the sign is in the vehicle's surrounding environment can provide contextual details. Identified signage can be associated with other objects in the vehicle's driving environment, which can be used to aid the vehicle in autonomous driving.

Aspects of the disclosure relate to determining a sign type of an unfamiliar sign. The system may include one or more processors. The one or more processors may be configured to receive an image and identify image data corresponding to a traffic sign in the image. The image data corresponding to the traffic sign may be input in a sign type model. The processors may determine that the sign type model was unable to identify a type of the traffic sign and determine one or more attributes of the traffic sign. The one or more attributes of the traffic sign may be compared to known attributes of other traffic signs and based on this comparison, a sign type of the traffic sign may be determined. The vehicle may be controlled in an autonomous driving mode based on the sign type of the traffic sign.

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Mountain View, CA, United States of America

Maya Kabkab