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US 12,586,230 B2Granted
Systems and methods for training a model for determining vehicle following distance
AssigneeGeotab Inc.
InventorsJoy Mazumder, Shashank Saurav, Javed Siddique, Mohammed Sohail Siddique
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Abstract
Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used to in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior.
Claims20
A system for training a model for determining a distance between two vehicles, the system comprising:
- at least one processor;
- at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor and storing processor-executable instructions which when executed by the at least one processor cause the system to:
- render image data based on simulation in a virtual environment, the image data including at least:
- a first set of images, each image in the first set of images including a representation of a respective first virtual vehicle from a perspective of a respective second virtual vehicle behind the respective first virtual vehicle, and each image in the first set of images associated with;
- a respective distance label indicating a distance between the respective first virtual vehicle and the respective second virtual vehicle and a respective vehicle presence label indicating that the respective first virtual vehicle is within a vehicle presence threshold distance of the respective second virtual vehicle;
- and
- a second set of images, each image in the second set of images including a representation from a perspective of a respective third virtual vehicle which is not in a following situation, and each image in the second set of images associated with:
- a respective distance label that is a null value or infinity label indicative of a non-following situation, and a respective vehicle presence label indicating that the respective third virtual vehicle is not within the vehicle presence threshold distance of another vehicle;
- generating respective vehicle presence labels and respective distance labels at the time of simulation in the virtual environment based on ground-truth relative positions of the respective first virtual vehicle and the respective second virtual vehicle, or the respective third virtual vehicle;
- evaluate a following distance loss function for at least one image in the first set of images and at least one image in the second set of images, the following distance loss function including:
- a first term comprising the vehicle presence label multiplied by a difference between a distance indicated in a respective distance label and a determined distance between two vehicles by the model for each respective image, and a second term representing a difference between the vehicle presence label and a determined vehicle presence for each respective image;
- and
- train the model by minimizing the following distance loss function over the first set of images and the second set of images.
10 more elements
A method for training a model for determining a distance between two vehicles, the method comprising:
- rendering image data based on simulation in a virtual environment, the image data including at least:
- a first set of images, each image in the first set of images including a representation of a respective first virtual vehicle from a perspective of a respective second virtual vehicle behind the respective first virtual vehicle, and each image in the first set of images associated with a distance label indicating a distance between the respective first virtual vehicle and the respective second virtual vehicle and a respective vehicle presence label indicating that the respective first virtual vehicle is within a vehicle presence threshold distance of the respective second virtual vehicle;
- and
- a second set of images, each image in the second set of images including a representation from a perspective of a respective third virtual vehicle which is not in a following situation, and each image in the second set of images associated with:
- a respective distance label that is a null value or infinity label indicative of a non-following situation, and a respective vehicle presence label indicating that the respective third virtual vehicle is not within the vehicle presence threshold distance of another vehicle;
- generating respective vehicle presence labels and respective distance labels at the time of simulation in the virtual environment based on ground-truth relative position of the respective first virtual vehicle and the respective second virtual vehicle, or the respective third virtual vehicle;
- evaluating a following distance loss function for at least one image in the first set of images and at least one image in the second set of images, the following distance loss function including;
- a first term comprising the vehicle presence label multiplied by a difference between a distance indicated in a respective distance label and a determined distance between two vehicles by the model for each respective image, and a second term representing a difference between the vehicle presence label and a determined vehicle presence for each respective image;
- and
- training the model by minimizing the following distance loss function over the first set of images and the second set of images.
7 more elements
Dependencies are inferred from the claim text.