Evolving a Real-World Vehicle Warning System (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. This paper describes three advances toward evolving neural networks with NEAT (NeuroEvolution of Augmenting Topologies) to warn about such crashes in real-world environments. First, NEAT was evaluated in a complex, dynamic simulation with other cars, where it outperformed three hand-coded strawman warning policies and generated warning levels comparable with those of an open-road warning system. Second, warning networks were trained using raw pixel data from a simulated camera. Surprisingly, NEAT was able to generate warning networks that performed similarly to those trained with higher-level input and still outperformed the baseline hand-coded warning policies. Third, the NEAT approach was evaluated in the real world using a robotic vehicle testbed. Despite noisy and ambiguous sensor data, NEAT successfully evolved warning networks using both laser rangefinders and visual sensors. The results in this paper set the stage for developing warning networks for real-world traffic, which may someday save lives in real vehicles.
In Proceedings of the Genetic and Evolutionary Computation Conference 2006.

Nate Kohl Ph.D. Alumni nate [at] natekohl net
Risto Miikkulainen Faculty risto [at] cs utexas edu
Rini Sherony Formerly affiliated Collaborator rini sherony [at] tema toyota com
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
NEAT C++ The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i... 2010