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

• URL: http://arxiv.org/abs/2405.03995v1
• Date: Tue, 7 May 2024 04:17:04 GMT
• Title: Deep Event-based Object Detection in Autonomous Driving: A Survey
• Authors: Bingquan Zhou, Jie Jiang,
• Abstract summary: Event cameras have emerged as promising sensors for autonomous driving due to their low latency, high dynamic range, and low power consumption. This paper provides an overview of object detection using event data in autonomous driving, showcasing the competitive benefits of event cameras.
• Score: 7.197775088663435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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