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

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Engineering


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.


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Gishkori S (2020) Graph Signal Processing-Based Imaging for Synthetic Aperture Radar in IEEE Geoscience and Remote Sensing Letters

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Gishkori S (2020) Imaging Moving Targets for a Forward-Scanning Automotive SAR in IEEE Transactions on Aerospace and Electronic Systems

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Gishkori S (2019) Imaging for a Forward Scanning Automotive Synthetic Aperture Radar in IEEE Transactions on Aerospace and Electronic Systems

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Gishkori S (2019) Pseudo-Zernike Moments Based Sparse Representations for SAR Image Classification in IEEE Transactions on Aerospace and Electronic Systems

Description We have developeed a new way to form a high quality radar image of the road in front of a moving vehicle. We have extended this technique to objects that are moving in the scene ahead of the vehicle. We have also developed a new way of improving the radar image (which works in all weathers) by combining it with video data (in good weather).
Exploitation Route The tevhniques have the potential to improve the sensing capability of future autonomous systems from driverless cars to aircraft landing systems.
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Transport

URL https://www.ipo.gov.uk/p-ipsum/Case/ApplicationNumber/GB1711427.3
Description A UK patent on the radar imaging technique was granted to Jaguar landrover, c.f. https://www.ipo.gov.uk/p-ipsum/Case/ApplicationNumber/GB1711427.3
First Year Of Impact 2020
Sector Transport
Title A Radar system for use in a vehicle 
Description An image processor (18,Fig.12a), system, or method for generating a radar image of a scene ahead or behind a direction of travel of a vehicle (10,Fig.12a). The scene comprises one or more radar reflective objects (16,Fig.12a). A radar scan is received at each of a plurality of different positions along the direction of travel of the vehicle. The radar scans are sequentially received as the vehicle travels along the direction of travel, whereby to provide a plurality of radar scans which define a synthetic aperture. The invention further comprises synthetic aperture radar processing means (18,Fig.12a) to combine each of the plurality of radar scans, whereby each radar scan comprises a plurality of radar signals for each of a plurality of different angular beam directions of a radar transmitting means or of the radar receiving means of the radar system, relative to the scene. The synthetic aperture radar processing means further generates a radar image of the scene ahead of or behind the direction of travel of the vehicle based on the combined plurality of radar scans across the synthetic aperture. This invention relates to both Spot and Strip SAR techniques. 
IP Reference GB2564648 
Protection Patent application published
Year Protection Granted 2019
Licensed Commercial In Confidence
Impact Commercial In Confidence