Deep learning plays a key role in the race to autonomous vehicles. Although remarkable progress has been made, the vast majority of both existing theories and technologies have yet to transition to real-world scenarios, which introduce a huge variety of road, weather and lighting conditions as well as deviations of driver behavior.  

Nexar builds the world's largest open vehicle-to-vehicle (V2V) network by turning smartphones into connected AI dash-cams. Joining deep learning with millions of crowdsourced driving miles collected by our users, Nexar’s technology provides a new, safer driving experience with the potential of saving the lives of 1.3 million people who die on the road every year.

In this talk, we will share our journey to make the roads safer. We will examine some of the challenges we face, from a real-time collision avoidance system to learning autonomous driving policies for a safer driving experience. We will also share some of our joint research with the Berkeley Deep-Drive (BDD) Industry Consortium and present the Nexar challenge, in which we open some of our deep learning challenges to the outside world and invite aspiring researchers to test their chops, win prizes, and to join our mission to free the world of car accidents.