Crash Predicting Network with Graded Warning for Vehicle Collision Warning

Project ID: 1537-AP
Available for non-exclusive licensing

Background

Automobile accidents are the number-one cause of death for ages 3 to 33 in the United States today. Each driver has individual driving styles and habits that are difficult to predict, not to mention outside variables such as other vehicles, bad weather, and road conditions. Although it is extremely complex for artificial intelligence technology to make a judgment on the level of danger in each driving circumstance, such information is necessary in order to create an accurate collision predictor.

Invention Description

NeuroEvolution of Augmenting Topologies (NEAT) trains neural networks for sequential decision tasks. NEAT gains experience in predicting collisions using a simulator such as the Robot Auto Racing Simulator (RARS), which simulates vehicle dynamics (including skidding and traction) and interactions between multiple vehicles. The crash predictor uses NEAT's recurrent network to analyze the possibility of a crash, including driving off the road and colliding with other cars. The algorithms works with range-finder and sonar inputs, as well as raw visual data, and provides a graded warning.

Benefits

Crash prediction prevents automobile accidents.
Warnings are accurate and helpful to human drivers.
Warnings can be customized to driver, car and conditions.
Graded warning system helps judge danger level of a situation.

Features

By integrating over past states, can predict crashes that cannot be calculated only from a vehicle's current state
Trains both drivers and crash predictors
Uses raw visual input from a simulated camera to predict the time to crash

Market Potential/Applications

Automotive industries and transportation safety industry may market product for consumers. Defensive driving and driver education schools may use the invention as a teaching tool.

Development Stage

Lab/bench prototype

IP Status

Two U.S. patent application filed

UT Researcher

Risto P. Miikkulainen, Ph.D., Computer Sciences, The University of Texas at Austin
Kenneth O. Stanley, Ph.D., Computer Sciences, The University of Texas at Austin
Nathaniel F. Kohl, Computer Sciences, The University of Texas at Austin
Rini Sherony, Toyota Technical Center, USA, Inc.

OTC Contact Information

Jitendra Jain, Licensing Specialist
jjain@otc.utexas.edu
512-471-9055

Web Links

http://www.cs.utexas.edu/risto/