Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
International Journal of Computer Vision
Evolving neural networks through augmenting topologies
Evolutionary Computation
Efficient Reinforcement Learning Through Evolving Neural Network Topologies
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving neural networks for fractured domains
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolving competitive car controllers for racing games with neuroevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Real-time evolution of neural networks in the NERO video game
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
On-line neuroevolution applied to the open racing car simulator
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Optimized sensory-motor couplings plus strategy extensions for the TORCS car racing challenge
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Evolutionary optimization of a neural network controller for car racing simulation
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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Many serious automobile accidents could be avoided if drivers were warned of impending crashes before they occurred. In this paper, a vehicle warning system is evolved to predict such crashes in the RARS driving simulator. The NeuroEvolution of Augmenting Topologies (NEAT) method is first used to evolve a neural network driver that can autonomously navigate a track without crashing. The network is subsequently impaired, resulting in a driver that occasionally makes mistakes and crashes. Using this impaired driver, a crash predictor is evolved that can predict how far in the future a crash is going to occur, information that can be used to generate an appropriate warning level. The main result is that NEAT can successfully evolve a warning system that takes into account the recent history of inputs and outputs, and therefore makes few errors. Experiments were also run to compare training offline from previously collected data with training online in the simulator. While both methods result in successful warning systems, offline training is both faster and more accurate. Thus, the results in this paper set the stage for developing crash predictors that are both accurate and able to adapt online, which may someday save lives in real vehicles.