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视场之外的视野:提高智能网络物理系统安全性的协作感知系统。

Vision beyond the Field-of-View: A Collaborative Perception System to Improve Safety of Intelligent Cyber-Physical Systems.

机构信息

Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea.

School of Innovation, Design, and Engineering, Malardalen University, 72220 Vasteras, Sweden.

出版信息

Sensors (Basel). 2022 Sep 1;22(17):6610. doi: 10.3390/s22176610.

Abstract

Cyber-physical systems (CPSs) that interact with each other to achieve common goals are known as collaborative CPSs. Collaborative CPSs can achieve complex goals that individual CPSs cannot achieve on their own. One of the examples of collaborative CPSs is the vehicular cyber-physical systems (VCPSs), which integrate computing and physical resources to interact with each other to improve traffic safety, situational awareness, and efficiency. The perception system of individual VCPS has limitations on its coverage and detection accuracy. For example, the autonomous vehicle's sensor cannot detect occluded objects and obstacles beyond its field of view. The VCPS can combine its own data with other collaborative VCPSs to enhance perception, situational awareness, accuracy, and traffic safety. This paper proposes a collaborative perception system to detect occluded objects through the camera sensor's image fusion and stitching technique. The proposed collaborative perception system combines the perception of surrounding autonomous driving systems (ADSs) that extends the detection range beyond the field of view. We also applied logistic chaos map-based encryption in our collaborative perception system in order to avoid the phantom information shared by malicious vehicles and improve safety in collaboration. It can provide the real-time perception of occluded objects, enabling safer control of ADSs. The proposed collaborative perception can detect occluded objects and obstacles beyond the field of view that individual VCPS perception systems cannot detect, improving the safety of ADSs. We investigated the effectiveness of collaborative perception and its contribution toward extended situational awareness on the road in the simulation environment. Our simulation results showed that the average detection rate of proposed perception systems was 45.4% more than the perception system of an individual ADS. The safety analysis showed that the response time was increased up to 1 s, and the average safety distance was increased to 1.2 m when the ADSs were using collaborative perception compared to those scenarios in which the ADSs were not using collaborative perception.

摘要

相互协作以实现共同目标的 系统被称为协同 系统。协同 系统可以实现单个 系统无法独自实现的复杂目标。协同 系统的一个例子是车联网 系统(VCPS),它集成了计算和物理资源,相互协作以提高交通安全、态势感知和效率。单个 VCPS 的感知系统在覆盖范围和检测精度上存在局限性。例如,自动驾驶车辆的传感器无法检测到其视野之外的被遮挡物体和障碍物。VCPS 可以将其自身的数据与其他协同 VCPS 相结合,以增强感知、态势感知、准确性和交通安全。本文提出了一种协同感知系统,通过摄像头传感器的图像融合和拼接技术来检测被遮挡的物体。所提出的协同感知系统结合了周围自动驾驶系统(ADS)的感知,扩展了检测范围,超出了视野范围。我们还在协同感知系统中应用了基于 logistic 混沌映射的加密技术,以避免恶意车辆共享的幻影信息,并提高协作中的安全性。它可以提供被遮挡物体的实时感知,使 ADS 能够更安全地控制。所提出的协同感知可以检测到单个 VCPS 感知系统无法检测到的被遮挡物体和障碍物,从而提高 ADS 的安全性。我们在仿真环境中研究了协同感知的有效性及其对道路扩展态势感知的贡献。我们的仿真结果表明,与单个 ADS 的感知系统相比,所提出的感知系统的平均检测率提高了 45.4%。安全性分析表明,与不使用协同感知的场景相比,当 ADS 使用协同感知时,响应时间增加了 1 秒,平均安全距离增加到 1.2 米。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f8f/9460724/c645d351a443/sensors-22-06610-g001.jpg

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