TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
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TruPercept：基于合成数据的自动驾驶车辆协作感知的信任建模
DOI：
10.1109/iv47402.2020.9304695
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Steven L. Waslander
中科院分区：
文献类型：
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作者：
Braden Hurl;Robin Cohen;K. Czarnecki;Steven L. Waslander
自动驾驶汽车 (AV) 的车间通信在感知鲁棒性方面具有显着优势。我们提出了一种让 AV 交流感知观察的新方法，并通过提供报告的同行的信任模型进行调整。基于本地验证的报告的物体检测的准确性，可以融合通信的消息，以增强视线之外和距自我车辆较远距离的感知性能。还提出了一个新的合成数据集，可用于测试合作感知。 TruPercept 数据集包含不可靠和恶意的行为场景，用于试验合作感知引入的一些挑战。 TruPercept 运行时和评估框架允许模块化组件替换，以促进消融研究以及我们能够展示的新信任场景的创建。
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communicate perceptual observations, tempered by trust modelling of peers providing reports. Based on the accuracy of reported object detections as verified locally, communicated messages can be fused to augment perception performance beyond line of sight and at great distance from the ego vehicle. Also presented is a new synthetic dataset which can be used to test cooperative perception. The TruPercept dataset includes unreliable and malicious behaviour scenarios to experiment with some challenges cooperative perception introduces. The TruPercept runtime and evaluation framework allows modular component replacement to facilitate ablation studies as well as the creation of new trust scenarios we are able to show.
DOI：
10.1109/ivs.2019.8813811
发表时间：
2019-06
期刊：
2019 IEEE Intelligent Vehicles Symposium (IV)
影响因子：
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作者：
Eduardo Arnold;Omar Y. Al-Jarrah;M. Dianati;Saber Fallah;David Oxtoby;A. Mouzakitis
通讯作者：
Eduardo Arnold;Omar Y. Al-Jarrah;M. Dianati;Saber Fallah;David Oxtoby;A. Mouzakitis