Motion Prediction for Autonomous Vehicles Using ResNet-Based Model

Abstract

Autonomous vehicles (AVs) are expected to greatly redefine the future of transportation. However, before people fully realize the benefits of autonomous vehicles, there are still major engineering challenges to be solved. One of the challenges is to build models that reliably predict the movement of the vehicle and its surrounding objects. In this paper, we proposed our ML policy to fully control a Self Driving Vehicle (SDV). The policy is a CNN architecture based on ResNet50 which is invoked by the SDV to obtain the next command to execute. In each step, we predict several different trajectories and their probabilities to assist us in decision-making. Compared with VGG16 and ResNet34, the simulation results demonstrate that our model based on ResN et50 improves the performance by 2.23% and 22.5%, respectively. It also shows that ResNet achieves better performance than VGG in the aspect of motion prediction. What's more, increasing the depth of the network can further improve the performance of the network.