Object Detection and Movement Prediction for Autonomous Vehicle: A Review
Radha Pandey, Arun Malik · 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC) · 2021
The region of computer vision is arising continuously to provide better interaction among humans and machines. One of the most exciting things in modern times is the Autonomous vehicle in which no intervention from a human is required to drive a car. For this, various factors need to be considered such as object detection, collision detection, vehicle/-pedestrian movement predictions, road bend prediction. For object detection, we have multiple architectures like Fast R-CNN, YOLO, SSD. Many research works is going on in the field of user personalization in an autonomous vehicle. This review paper aims to summarize the methodologies and machine learning models applied in the design of the autonomous driving system. We first provide the overview of various methods and algorithms and then compare the performance of models based on the time taken by them to predict a single image. In this paper, for the movement prediction few deep learning models are discussed followed by research gaps in some of the papers and their potential solutions.