NEAT: Evolving Vehicle Warning Systems
Active from 2004 - 2006

Many serious automobile accidents could be avoided if drivers were warned of impending crashes before they occur. Creating such warning systems by hand, however, is a difficult and time-consuming task. The goal of this project is to evolve neural networks with NEAT (NeuroEvolution of Augmenting Topologies) to warn about such crashes in real-world environments.

NEAT was used to train warning networks in a complex, dynamic simulation of both open-road driving as well as driving with other cars. Different sensor modalities were evaluated, resulting in the suprising discovery that NEAT was able to successfully generate warning networks using only raw pixel data from a simulated camera. This approach was also implemented on a real robot to determine how well this approach scales from simulation to the real world.

See vehicle warning movie page for a demo.

Risto Miikkulainen Faculty risto [at] cs utexas edu
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
Nate Kohl Ph.D. Alumni nate [at] natekohl net
Rini Sherony Former Collaborator rini sherony [at] tema toyota com
Evolving a Real-World Vehicle Warning System Nate Kohl, Kenneth Stanley, Risto Miikkulainen, Michael Samples, and Rini Sherony In Proceedings of the Genetic and Evolutionary Computation Conference, 2006. 2006

Neuroevolution of an Automobile Crash Warning System Kenneth Stanley, Nate Kohl, Rini Sherony, and Risto Miikkulainen In Proceedings of the Genetic and Evolutionary Computation Conference, 2005. 2005