Plusai Limited

Inderjot Singh Saggu

Ankur Agarwal

Anurag Ganguli

Mianwei Zhou

Anurag Paul

Abhishek Bhatia

Timothy Patrick Daly, Jr

Amit Kumar

Lichun Yang

Sai Akhil Suggu

This application is directed to augmenting training images used for generating vehicle driving models. A computer system obtains a first image of a road, identifies within the first image a drivable area of the road, obtains an image of a traffic safety object, and determines a detour path on the drivable area. The computer system determines positions of a plurality of traffic safety objects to be placed adjacent to the detour path, and generates a second image from the first image by adaptively overlaying a respective copy of the image of the traffic safety object at each of the positions of the plurality of traffic safety objects on the drivable area within the first image. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving a vehicle (e.g., at least partial autonomously).

Data augmentation for detour path configuring

In some embodiments, a method can include executing a first machine learning model to detect at least one lane in each image from a first set of images. The method can further include determining an estimate location of a vehicle for each image, based on localization data captured using at least one localization sensor disposed at the vehicle. The method can further include selecting lane geometry data for each image, from a map and based on the estimate location of the vehicle. The method can further include executing a localization model to generate a set of offset values for the first set of images based on the lane geometry data and the at least one lane in each image. The method can further include selecting a second set of images from the first set of images based on the set of offset values and a previously-determined offset threshold.

Methods and apparatus for automatic collection of under-represented data for improving a training of a machine learning model

This application is directed to augmenting training images used for generating a model for monitoring vehicle drivers. A computer system obtains a first image of a first driver in an interior of a first vehicle and separates, from the first image, a first driver image from a first background image of the interior of the first vehicle. The computer system obtains a second background image and generates a second image by overlaying the first driver image on the second background image. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for monitoring vehicle drivers. In some embodiments, at least one of the first driver image and the second background image is adjusted to match lighting conditions, average intensities, and sizes of the first driver image and the second background image.

Data augmentation for driver monitoring

This application is directed to augmenting training data used for vehicle driving modelling. A computer system obtains a first image of a road and identifies a drivable area of the road within the first image. The computer system obtains an image of an object and generates a second image from the first image by overlaying the image of the object over the drivable area. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving of a vehicle (e.g., at least partial autonomously). In some embodiments, the computer system applies machine learning to train a model using the corpus of training images and distributes the model to one or more vehicles. In use, the model processes road images captured by the one or more vehicles to facilitate vehicle driving.

Data augmentation for vehicle control

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining an image of an environment. A drivable area and boundary information associated with the environment are determined based on the image. At least one boundary for navigation of the environment is generated based on the drivable area and boundary information.

Determining lanes from drivable area

A computer system obtains a plurality of road images captured by one or more cameras attached to one or more vehicles. The one or more vehicles execute a model that facilitates driving of the one or more vehicles. For each road image of the plurality of road images, the computer system determines, in the road image, a fraction of pixels having an ambiguous lane marker classification. Based on the fraction of pixels, the computer system determines whether the road image is an ambiguous image for lane marker classification. In accordance with a determination that the road image is an ambiguous image for lane marker classification, the computer system enables labeling of the image and adds the labeled image into a corpus of training images for retraining the model.

Ambiguous lane detection event miner

This application is directed to generic obstacle detection for at least partially autonomous vehicle driving. A first vehicle obtains a road image. The road image includes a road surface along which the first vehicle is travelling. The first vehicle identifies one or more identifiable objects on the road surface in the road image. The first vehicle detects a plurality of objects on the road surface in the road image. The first vehicle eliminates the one or more identifiable objects from the plurality of objects in the road image to determine one or more unidentifiable objects on the road surface in the road image. The first vehicle at least partially autonomously drives the first vehicle by treating the one or more unidentifiable objects differently from the one or more identifiable objects.

Generic obstacle detection in drivable area

An autonomous vehicle can obtain sensor data. Upon determining that the autonomous vehicle is in a lane adjacent a shoulder, and there is an object in the shoulder, the autonomous vehicle can determine if performing a lane change maneuver out of the lane prior to the autonomous vehicle being positioned adjacent to the object is feasible. If it is, the lane change maneuver can be performed. If it is not, a nudge maneuver and/or a deceleration can be performed.

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Cupertino, CA, United States of America

Inderjot Singh Saggu