Crash prediction network with visual input for vehicle
AssigneesToyota Motor Engineering & Manufacturing North America, Inc., Board of Regents, The University of Texas System
InventorsRini Sherony, Risto P. Miikkulainen, Kenneth O. Stanley, Nathaniel F. Kohl
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Abstract
A method for facilitating the avoidance of a vehicle collision with an object includes the following steps: a) providing an environment for generating training examples, b) evolving a good driver using a visual input, c) evolving a crash predictor using a visual input, and d) outputting a warning signal.
Claims20
A method for facilitating the avoidance of a vehicle collision with an object comprising the steps of:
- a) providing a training environment;
- b) evolving a good driver using a visual input, wherein the step of evolving a good driver includes the steps of:
- e) providing an initial population of networks;
- f) generating visual sensory signals;
- h) inputting the visual sensory signals to-the a neural network;
- h) calculating a fitness value:
- i) repeating steps f-h a plurality of times for each neural network;
- j) determining which neural network to discard and which neural network to reproduce;
- k) forming a new population by discarding and reproducing;
- l) repeating steps i-k a plurality of times;
- c) evolving a crash predictor using a visual input;
- d) outputting a warning signal.
A method for facilitating the avoidance of a vehicle collision with an object comprising the steps of:
- a) providing a training environment:
- b) evolving a good driver using a visual input:
- c) evolving a crash predictor using a visual input wherein the step of evolving a crash predictor includes the steps of
- m) providing an initial population of networks;
- n) generating visual sensory signals;
- o) inputting the visual sensory signals to the a neural network;
- p) forming targeted outputs based on when a crash occurs;
- q) calculating a fitness value;
- r) repeating steps m-q a plurality of times for each neural network;
- s) determining which neural network to discard and which neural network to reproduce;
- t) forming a new population by discarding and reproducing;
- u) repeating steps r-t a plurality of times;
- and
- d) outputting a warning signal.
Dependencies are inferred from the claim text.