mlsanityData to 7 Oct 2026

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

arXiv 2405.03995 ↗First posted 7 May 2024cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 41 of its 60 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

41 of 60 references.

References · 41

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29 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Adaptive Event Stream Slicing for Open-Vocabulary Event-Based Object Detection via Vision-Language Knowledge Distillationcited by 8
  2. 2025When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Networkcited by 3
  3. 2026Generative Anonymization in Event Streamscited by 0
  4. 2026Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target trackingcited by 0