Deep Event-based Object Detection in Autonomous Driving: A Survey

TL;DR

This survey reviews the use of event cameras for object detection in autonomous driving, highlighting their advantages and the challenges in developing efficient, low-latency detection methods with asynchronous event data.

Contribution

It provides a comprehensive overview of current techniques and challenges in event-based object detection for autonomous driving applications.

Findings

Event cameras offer low latency and high dynamic range benefits.

Utilizing asynchronous event data remains challenging for lightweight detection.

Event-based detection methods are competitive with traditional approaches.

Abstract

Object detection plays a critical role in autonomous driving, where accurately and efficiently detecting objects in fast-moving scenes is crucial. Traditional frame-based cameras face challenges in balancing latency and bandwidth, necessitating the need for innovative solutions. Event cameras have emerged as promising sensors for autonomous driving due to their low latency, high dynamic range, and low power consumption. However, effectively utilizing the asynchronous and sparse event data presents challenges, particularly in maintaining low latency and lightweight architectures for object detection. This paper provides an overview of object detection using event data in autonomous driving, showcasing the competitive benefits of event cameras.