Motion Prediction for Autonomous Vehicle using Deep Learning Architecture and Transfer Learning

Abstract

In the current scenario, Autonomous Vehicle (AV) technology has become one of the more futuristic approaches in the automotive industry since it aims to enhance driving safety, driving comfort, and its economy, adding to reduced obstacle collision or traffic accident rate. Motion planning contributes a vital part in autonomous driving, serving as a fundamental building block enabling the AV to move further. Though there are several traditional approaches to implement motion planning, yet challenges remain regarding guaranteed performance and safety under all driving circumstances. With the impressive advancement of deep learning technologies, many researchers have tried to develop end-to-end motion planning approaches using deep learning, which generally employ Deep Neural Networks (DNNs) to directly map the raw sensor data (e.g., point clouds and images) to planned trajectories(e.g., yaw velocity and steering angle). However, accurate motion prediction is still critical for autonomous driving where there’s a transfer learning approach which emphasizes on reusing a trained model for various applications. This could possibly improve the accuracy of motion prediction steadily. Considering this in mind, a transfer learning motion prediction approach for autonomous driving employing the DNN is proposed here, also providing visual element representing the predicted path, by streamlining the use of the toolkit available at hand. The output responses are discussed and compared employing the existing prediction model and the proposed model. The experimental results exhibited improved performance of motion prediction of the proposed transfer learning approach proving better accuracy comparitively with the existing Resnet50 model.