Deep Event-based Object Detection in Autonomous Driving: A Survey
It addresses the problem of efficient and accurate object detection for autonomous driving systems, but it is incremental as it is a survey paper.
This survey tackles the challenge of object detection in autonomous driving by reviewing event-based cameras, which offer low latency and high dynamic range compared to traditional frame-based methods, highlighting their competitive benefits.
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.