Crash prediction network with graded warning for vehicle

1. A method for facilitating the avoidance of a vehicle collision with an object comprising the steps of:

a) providing a neural network;

b) evolving a good driver wherein the step of evolving a good driver includes the steps of:

e) providing a biasing input;

f) generating sensory signals;

g) inputting the sensory signals and biasing input to the neural network;

h) calculating a reward value;

i) calculating a fitness value determining the retention of the neural network;

j) repeating steps e-i a plurality of times;

c) evolving a crash predictor;

d) outputting a graded warning signal.

2. The method of claim 1 wherein the biasing input is 1.

3. The method of claim 1 wherein the sensory signals are rangefinder data signals.

4. The method of claim 3 wherein the rangefinder data signals are converted from simulated driving data.

5. The method of claim 1 wherein the fitness is calculated in proportion to a distance traveled and a damage incurred over time spent off the road.

6. The method of claim 1 wherein the steps e-h are repeated 1000 times for a single trial.

7. The method of claim 6 including a plurality of trials.

8. A method for facilitating the avoidance of a vehicle collision with an object comprising the steps of:

a) providing a neural network;

b) evolving a good driver;

c) evolving a crash predictor wherein the step of evolving a crash predictor includes the steps of:

j) calculating a biasing input

k) generating sensory signals;

l) inputting the sensory signals and biasing input to the neural network;

m) calculating a temporal value;

n) inputting the calculated temporal value into a queue;

o) providing an ideal temporal queue;

p) determining if a crash occurs;

q) calculating a reward value

r) calculating a fitness value determining the retention of the neural network;

s) repeating steps k-r a plurality of times;

d) outputting a graded warning signal.

9. The method of claim 8 wherein the biasing input is 1 .

10. The method of claim 8 wherein the sensory signals are rangefinder data signals.

11. The method of claim 10 wherein the rangefinder data signals are converted from simulated driving data.

12. The method of claim 8 wherein the temporal value is calculated from an output of the neural network.

13. The method of claim 8 wherein the reward value is calculated according to the formula:

R=Σ t 25= 1 25 −|q t −I L |/25

Wherein q t is a prediction in the queue and I L is an ideal prediction where 1<L<25.

14. The method of claim 8 wherein the reward is assigned a relative value such that desirable characteristics are favored in the neural network.

15. The method of claim 8 wherein the fitness is computed by accumulating a total reward R tot equaling the fitness of the entire evaluation including all crashes and prediction sand wherein R tot is modified at each time step in one of two cases:

1 ) If the car crashes, R tot increases according to how close the actual prediction queue is to the ideal prediction queue, and

2 ) If the car does not crash, a small bonus is added to R tot if the oldest prediction in the queue was that no crash would occur.

16. The method of claim 3 wherein the fitness is calculated according to the formula:

Ē=Σ n t=1 ( O t −I t ) 2 /n

wherein Ē is an average error over n timesteps, I t is an ideal prediction at time step t and O t is a prediction output.

17. A method for facilitating the avoidance of a vehicle collision with an object comprising the steps of:

a) providing a neural network;

b) evolving a good driver including the steps of:

i) providing a biasing input;

ii) generating sensory signals;

iii) inputting the sensory signals and biasing input to the neural network;

iv) calculating a reward value;

v) calculating a fitness value determining the retention of the neural network;

vi) repeating steps e-i a plurality of times;

c) evolving a crash predictor including the steps of:

vii) calculating a biasing input

viii) generating sensory signals;

ix) inputting the sensory signals and biasing input to the neural network;

x) calculating a temporal value;

xi) inputting the calculated temporal value into a queue;

xii) providing an ideal temporal queue;

xiii) determining if a crash occurs;

xiv) calculating a reward value

xv) calculating a fitness value determining the retention of the neural network;

xvi) repeating steps k-r a plurality of times; and

d) outputting a graded warning signal.