Optical Flow Based Motion Detection for Autonomous Driving

TL;DR

This paper presents a neural network-based approach utilizing optical flow for motion detection in autonomous driving, effectively identifying distant objects with high accuracy and supporting decision-making in highway scenarios.

Contribution

It introduces a neural network model trained on optical flow data for motion classification, demonstrating high accuracy and applicability to both distant and nearby vehicles.

Findings

High accuracy in distant vehicle motion detection

Effective performance on nearby vehicles

Implementation using PyTorch with open tools

Abstract

Motion detection is a fundamental but challenging task for autonomous driving. In particular scenes like highway, remote objects have to be paid extra attention for better controlling decision. Aiming at distant vehicles, we train a neural network model to classify the motion status using optical flow field information as the input. The experiments result in high accuracy, showing that our idea is viable and promising. The trained model also achieves an acceptable performance for nearby vehicles. Our work is implemented in PyTorch. Open tools including nuScenes, FastFlowNet and RAFT are used. Visualization videos are available at https://www.youtube.com/playlist?list=PLVVrWgq4OrlBnRebmkGZO1iDHEksMHKGk .