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Event-Based Neuromorphic Vision for Autonomous Driving: A Paradigm Shift for Bio-Inspired Visual Sensing and Perception
IEEE Signal Processing Magazine ( IF 14.9 ) Pub Date : 2020-07-01 , DOI: 10.1109/msp.2020.2985815
Guang Chen , Hu Cao , Jorg Conradt , Huajin Tang , Florian Rohrbein , Alois Knoll

As a bio-inspired and emerging sensor, an event-based neuromorphic vision sensor has a different working principle compared to the standard frame-based cameras, which leads to promising properties of low energy consumption, low latency, high dynamic range (HDR), and high temporal resolution. It poses a paradigm shift to sense and perceive the environment by capturing local pixel-level light intensity changes and producing asynchronous event streams. Advanced technologies for the visual sensing system of autonomous vehicles from standard computer vision to event-based neuromorphic vision have been developed. In this tutorial-like article, a comprehensive review of the emerging technology is given. First, the course of the development of the neuromorphic vision sensor that is derived from the understanding of biological retina is introduced. The signal processing techniques for event noise processing and event data representation are then discussed. Next, the signal processing algorithms and applications for event-based neuromorphic vision in autonomous driving and various assistance systems are reviewed. Finally, challenges and future research directions are pointed out. It is expected that this article will serve as a starting point for new researchers and engineers in the autonomous driving field and provide a bird's-eye view to both neuromorphic vision and autonomous driving research communities.

中文翻译:

用于自动驾驶的基于事件的神经形态视觉:仿生视觉传感和感知的范式转变

作为一种受生物启发的新兴传感器,基于事件的神经形态视觉传感器与标准的基于帧的相机相比具有不同的工作原理,这导致了低能耗、低延迟、高动态范围 (HDR)、和高时间分辨率。它通过捕获局部像素级光强度变化并产生异步事件流,实现了感知和感知环境的范式转变。从标准计算机视觉到基于事件的神经形态视觉,自动驾驶汽车视觉传感系统的先进技术已经开发出来。在这篇类似教程的文章中,对新兴技术进行了全面回顾。首先介绍了对生物视网膜的理解衍生出的神经形态视觉传感器的发展历程。然后讨论了事件噪声处理和事件数据表示的信号处理技术。接下来,回顾了自动驾驶和各种辅助系统中基于事件的神经形态视觉的信号处理算法和应用。最后,指出了挑战和未来的研究方向。预计本文将为自动驾驶领域的新研究人员和工程师提供一个起点,并为神经形态视觉和自动驾驶研究社区提供一个鸟瞰图。指出了挑战和未来的研究方向。预计本文将为自动驾驶领域的新研究人员和工程师提供一个起点,并为神经形态视觉和自动驾驶研究社区提供一个鸟瞰图。指出了挑战和未来的研究方向。预计本文将为自动驾驶领域的新研究人员和工程师提供一个起点,并为神经形态视觉和自动驾驶研究社区提供一个鸟瞰图。
更新日期:2020-07-01
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