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Engineering  |  FURI
FURI Spring 2018
Security

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

The research objective is the implementation of a Collision Avoidance System for automobiles using deep neural networks. The researchers have been able to generate the data set in Webots, and use it to train/test the predictive model, thus obtaining higher levels of accuracy compared to the previous simulation environment (VREP). The next goal is to publish a data set paper and to increase the realism of the scenarios by adding abnormal driving behaviours.

Cesar Tamayo

Havana, Cuba

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Deep Predictive Models of Furi Symposium Poster

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