Deep Learning based Object Detection Model for Autonomous Driving Research using CARLA Simulator

Autonomous vehicle research has grown exponentially over the years with researchers working on different object detection algorithms to realize safe and competent self-driving systems while legal authorities are simultaneously looking into the ways of mitigating the risks posed by fully autonomous vehicles. These advancements can result in a much safer commuting environment, reduced accidents and also eliminate the necessity for human driving. The creation of data and access to data for autonomous driving research is difficult challenge that research communities are facing. Hence, open source simulators such as the CARLA simulator (CAR Learning to Act) help us train and test models and to gain insights into autonomous driving with ease. This paper proposes the application of object detection algorithm on CARLA simulator to derive useful results for autonomous driving research. Further, the comparison of CARLA simulator with other available simulators, key players in the field of autonomous vehicle technology, state-of-the-art algorithms being used for autonomous driving, real time implementation challenges and future technologies are also discussed.