Motion Prediction for Autonomous Vehicles Using Deep Learning Techniques

Abstract

Autonomous vehicles require motion prediction of nearby traffic agents to guarantee secure navigation. By anticipating the movements of surrounding objects such as other vehicles, pedestrians, and bicycles, the autonomous vehicle can make informed decisions to prevent collisions, adjust speed and direction, and operate effectively in changing conditions. Motion prediction is an essential aspect of autonomous vehicle systems, contributing to increased safety, dependability, and efficiency. This work generates an effective mechanism to predict the movement direction of nearby traffic entities around our ego vehicle (the vehicle that we originally reference) using deep learning techniques and the inclusion of a LIDAR-based dataset.