End-to-End Crash Avoidance Deep IoT-based Solution

Abstract

Fully Autonomous Driving is considered as one of the difficult problems faced the automotive applications. It is forbidden due to the presence of some restricted Laws that prevent cars from being autonomous for the fear of accidents occurrence. However, researchers try to reach autonomous driving as a new area for research for the aim of having a strong push against these restricted Laws. Crash Avoidance functionality is one of the most important features in Self-Driving Cars that is partially integrated recently. We propose an end-to-end Crash Avoidance Deep IoT solution which is decomposed into two main parts: a) Detection Deep Neural Network which aims to detect accident occurrence in front of the ego-vehicle, and b) Accident Information Spreading IoT which is responsible for informing upcoming vehicles that there is an accident, then these vehicles will be able to take the reasonable actions either changing their routes, or changing their lanes avoiding crash. Due to the lack of Crash benchmarks, we build our own benchmark, depending only on a front camera, using ROS-Gazebo Simulation environment covering various crashes situations. In General, our proposed idea is the first solution that merges Deep Learning with IoT in automotive applications.