CRASH PREDICTION NETWORK WITH VISUAL INPUT FOR VEHICLE

• US 20080172177A1
• Filed: 07/18/2006
• Published: 07/17/2008
• Est. Priority Date: 07/19/2005
• Status: Active Grant

• Current Assignee: Toyota Motor Corporation, Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Toyota Technical Center USA, Inc. (Toyota Motor Corporation), Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Stanley, Kenneth O., Sherony, Rini, Kohl, Nathaniel F., Miikkulainen, Risto P.
• Granted Patent: US 7,613,569 B2
• Time in Patent Office: Days
• US Class Current: 701/301
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

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.

Claims

1. 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;

d) outputting a warning signal.

2. The method of claim 1 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 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.

3. The method of claim 2 wherein the fitness is calculated in proportion to a distance traveled and a damage incurred over time spent off the road.

4. The method of claim 3 wherein the sensory signals are visual pixels.

5. The method of claim 4 including the step of scaling the visual sensory signals following the step of generating the visual sensory signals.

6. The method of claim 4 wherein the step of scaling the visual sensory signals includes assigning each pixel a numerical value.

7. The method of claim 6 wherein the numerical value is from zero to one.

8. The method of claim 4 wherein the visual pixels are converted from simulated driving data.

9. The method of claim 4 wherein the visual pixels are generated from a CMOS or CCD sensor.

10. The method of claim 1 wherein the visual sensory signal is inputted online.

11. The method of claim 1 wherein the visual sensory signal is inputted offline.

12. The method of claim 1 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 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.

13. The method of claim 12 wherein the fitness is calculated in proportion to a difference in the targeted output and the actual output of the network.

14. The method of claim 12 wherein the sensory signals are visual pixels.

15. The method of claim 12 including the step of scaling the visual sensory signals following the step of generating the visual sensory signals.

16. The method of claim 15 wherein the step of scaling the visual sensory signals includes assigning each pixel a numerical value.

17. The method of claim 16 wherein the numerical value is from zero to one.

18. The method of claim 14 wherein the visual pixels are converted from simulated driving data.

19. The method of claim 14 wherein the visual pixels are generated from a CMOS or CCD sensor.

20. The method of claim 12 wherein the visual sensory signal is inputted online.

21. The method of claim 12 wherein the visual sensory signal is inputted offline.