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

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arxiv 2405.03995 v1 pith:32ZN5KS5 submitted 2024-05-07 cs.CV

classification cs.CV
keywords autonomousdetectiondrivingeventobjectcameraslatencychallenges
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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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Cited by 1 Pith paper

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  1. When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A multimodal network combining an asynchronous graph neural network for events with a CNN for RGB images detects driving anomalies with competitive accuracy and lower response time than prior methods, but only on simu...

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