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...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Nate Kohl, Kenneth O. Stanley, Risto Miikkulainen, Michael E. Samples, Rini Sherony
• Keywords: GECCO 2006, Optimization, Warning Networks, Warning Policies, Warning System