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TASCC: Pervasive low-TeraHz and Video Sensing for Car Autonomy and Driver Assistance (PATH CAD)

TASCC：用于汽车自主和驾驶辅助的普遍低太赫兹和视频传感 (PATH CAD)

基本信息

批准号：
EP/N012372/1
负责人：
Marina Gashinova
金额：
$ 108.68万
依托单位：
University of Birmingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN012372%2F1
关键词：
TASCC Pervasive low TeraHz Video

项目摘要

This project combines novel low-THz (LTHz) sensor development with advanced video analysis, fusion and cross learning. Using the two streams integrated within the sensing, information and control systems of a modern automobile, we aim to map terrain and identify hazards such as potholes and surface texture changes in all weathers, and to detect and classify other road users (pedestrians, car, cyclists etc.). The coming era of autonomous and assisted driving necessitates new all-weather technology. Advanced concepts of interaction between the sensed and processed data, the control systems and the driver can lead to autonomy in decision and control, securing all the needed information for the driver to intervene in critical situations. The aims are to improve road safety through increased situational awareness, and increase energy efficiency by reducing the emission of pollutants caused by poor control and resource use in both on and off-road vehicles. Video cameras remain at the heart of our system: there are many reasons for this: low cost, availability, high resolution, a large legacy of processing algorithms to interpret the data and driver/passenger familiarity with the output. However it is widely recognized that video and/or other optical sensors such as LIDAR (c.f. Google car) are not sufficient. The same conditions that challenge human drivers such as heavy rain, fog, spray, snow and dust limit the capability of electro-optical sensors. We require a new approach.The key second sensor modality is a low-THz radar system operating within the 0.3-1 THz frequency spectrum. By its very nature radar is robust to the conditions that limit video. However it is the relatively short wavelength and wide bandwidth of this LTHz radar with respect to existing automotive radar systems that can bring key additional capabilities. This radar has the potential to provide: (i) imagery that is closer to familiar video than those provided by a conventional radar, and hence can begin to exploit the vast legacy of image processing algorithms; (ii) significantly improved across-road image resolution leading to correspondingly significant improvements in vehicle, pedestrian and other 'actor' (cyclists, animals etc.) detection and classification; (iii) 3D images that can highlight objects and act as an input to the guidance and control system; (iv) analysis of the radar image features, such as shadows and image texture that will contribute to both classification and control. The project is a collaboration between three academic institutions - the University of Birmingham with its long standing excellence in automotive radar research and radar technologies, the University of Edinburgh with world class expertise in signal processing and radar imaging and Heriot-Watt University with equivalent skill in video analytics, LiDAR and accelerated algorithms. The novel approach will be based on a fusion of video and radar images in a cross-learning cognitive process to improve the reliability and quality of information acquired by an external sensing system operating in all-weather, all-terrain road conditions without dependency on navigation assisting systems.

该项目将新型低THz（LTHz）传感器开发与先进的视频分析、融合和交叉学习相结合。使用集成在现代汽车的传感，信息和控制系统中的两个流，我们的目标是绘制地形图，识别各种天气下的坑洼和表面纹理变化等危险，并检测和分类其他道路使用者（行人，汽车，骑自行车的人等）。即将到来的自动驾驶和辅助驾驶时代需要新的全天候技术。传感和处理数据、控制系统和驾驶员之间的先进交互概念可以实现决策和控制的自主性，确保驾驶员在关键情况下进行干预所需的所有信息。其目的是通过提高对情况的认识来改善道路安全，并通过减少公路和越野车辆因控制不善和资源使用不当而造成的污染物排放来提高能源效率。摄像机仍然是我们系统的核心：这有很多原因：低成本，可用性，高分辨率，大量的处理算法来解释数据以及驾驶员/乘客对输出的熟悉程度。然而，人们广泛认识到，视频和/或其他光学传感器，例如激光雷达（参见谷歌汽车（Google car）是不够的。挑战人类驾驶员的相同条件，如大雨，雾，喷雾，雪和灰尘，限制了光电传感器的能力。我们需要一种新的方法。关键的第二传感器模态是在0.3-1 THz频谱内工作的低THz雷达系统。就其本质而言，雷达对限制视频的条件具有鲁棒性。然而，相对于现有的汽车雷达系统，这种LTHz雷达相对较短的波长和较宽的带宽可以带来关键的额外功能。这种雷达有潜力提供：（i）比传统雷达提供的图像更接近熟悉的视频，因此可以开始利用图像处理算法的巨大遗产;（ii）显著改善的跨道路图像分辨率，从而相应地显著改善车辆、行人和其他“演员”（骑自行车的人、动物等）。检测和分类;三维图像，可以突出物体并作为制导和控制系统的输入;分析雷达图像特征，如有助于分类和控制的阴影和图像纹理。该项目是三个学术机构之间的合作-伯明翰大学在汽车雷达研究和雷达技术方面长期卓越，爱丁堡大学在信号处理和雷达成像方面拥有世界一流的专业知识，赫瑞瓦特大学在视频分析，激光雷达和加速算法方面拥有同等技能。该新方法将基于视频和雷达图像在交叉学习认知过程中的融合，以提高在全天候，全地形道路条件下运行的外部传感系统获取的信息的可靠性和质量，而不依赖于导航辅助系统。