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GECCO
2006
Springer

Evolving a real-world vehicle warning system

11 years 5 months ago
Evolving a real-world vehicle warning system
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 d...
Nate Kohl, Kenneth O. Stanley, Risto Miikkulainen,
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Nate Kohl, Kenneth O. Stanley, Risto Miikkulainen, Michael E. Samples, Rini Sherony
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