Bolstering the safety of self-driving cars with a deep learning-based object detection system

Self-driving cars, or autonomous vehicles, have long been earmarked as the next generation mode of transport. To enable the autonomous navigation of such vehicles in different environments, many different technologies relating to signal processing, image processing, artificial intelligence deep learning, edge computing, and IoT, need to be implemented. One of the largest concerns around the popularization of autonomous vehicles is that of safety and reliability. In order to ensure a safe driving experience for the user, it is essential that an autonomous vehicle accurately, effectively, and efficiently monitors and distinguishes its surroundings as well as potential threats to passenger safety. To this end, autonomous vehicles employ high-tech sensors, such as Light Detection and Ranging (LiDaR), radar, and RGB cameras that produce large amounts of data as RGB images and 3D measurement points, known as a "point cloud."