Multi-Sensor Fusion Using Deep Neural Networks for Robust Object Detection in Autonomous Driving

Nowadays, self-directed learning and automation are not restricted to human beings only. If you stare out at the automotive horizon, you can see a new, exciting era coming into the limelight: the age of self-driving cars. An age when humans will no longer need to keep their eyes on the road. No more concerns about distraction while driving or those stressful rush hour commutes, vehicles will whisk us where we want to go, blazingly fast and efficiently. Deep learning is one potential solution for object detection and scene perception problems, which can enable algorithm-driven and data-driven cars. This research paper presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. In this research paper, the theory underlying self-driving vehicles from a deep learning perspective and current implementations of self-driving cars, followed by their critical evaluations, are examined. Thus, in this paper, the gap between deep learning and self-driving cars is bridged through a comprehensive survey.