Embedded System Enabled Vehicle Collision Detection: An ANN Classifier

Abstract

An Autonomous vehicle depends on the combination of latest technology or ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning /Avoidance (FCW or FCA). The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. The concept behind a FCW algorithm is the measure of a distance or warning range which results in an alert to notify or inform the driver regarding the possible collision. The objective of this paper is to propose a collision warning model using the RTMaps framework. The proposed model is based on the forward facing automotive radar providing the sensed input values such as acceleration, velocity, and separation distance to a neural network based classifier algorithm which on the basis of supervised learning alerts the driver of a possible collision. The implementation, precision and accuracy of the classifier and regression algorithm is discussed and compared. The presented model is deployed on the Bluebox 2.0 platform with the RTMaps Embedded framework.