Vehicular threat detection based on image analysis
First Claim
1. A method for enhancing ability in a transportation-related context, the method comprising:
- receiving image data, at least some of which represents an image of a first vehicle;
determining vehicular threat information based at least in part on the image data, by;
identifying multiple threats to a user; and
identifying a first threat of the multiple threats that is more significant than at least one other of the multiple threats, by;
modeling multiple potential accidents that each correspond to one of the multiple threats to determine a severity associated with each potential accident based on a collision force associated with each potential accident, the collision force based at least on mass and acceleration; and
selecting the first threat based at least in part on which of the multiple potential accidents has the highest collision force; and
presenting the vehicular threat information via a wearable device of the user by instructing the user to avoid the first one of the multiple threats.
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Accused Products
Abstract
Techniques for ability enhancement are described. Some embodiments provide an ability enhancement facilitator system (“AEFS”) configured to enhance a user'"'"'s ability to operate or function in a transportation-related context as a pedestrian or a vehicle operator. In one embodiment, the AEFS is configured perform vehicular threat detection based at least in part on analyzing image data. An example AEFS receives data that represents an image of a vehicle. The AEFS analyzes the received data to determine vehicular threat information, such as that the vehicle may collide with the user. The AEFS then informs the user of the determined vehicular threat information, such as by transmitting a warning to a wearable device configured to present the warning to the user.
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Citations
47 Claims
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1. A method for enhancing ability in a transportation-related context, the method comprising:
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receiving image data, at least some of which represents an image of a first vehicle; determining vehicular threat information based at least in part on the image data, by; identifying multiple threats to a user; and identifying a first threat of the multiple threats that is more significant than at least one other of the multiple threats, by; modeling multiple potential accidents that each correspond to one of the multiple threats to determine a severity associated with each potential accident based on a collision force associated with each potential accident, the collision force based at least on mass and acceleration; and selecting the first threat based at least in part on which of the multiple potential accidents has the highest collision force; and presenting the vehicular threat information via a wearable device of the user by instructing the user to avoid the first one of the multiple threats. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47)
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Specification