FURI | Spring 2018
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.
Computer systems engineering
Hometown: Havana, Cuba
Graduation date: Spring 2020