Project description
Radar sensors key to self-driving cars
Autonomous car driving systems are constantly rising, poised to reach a EUR 6 trillion market by 2050. However, self-driving systems need improved long-range depth sensing conventionalities. Existing advanced optical technologies have low performance, are vulnerable to weather conditions and are expensive. An alternative solution is the millimetre wave multiple-input multiple-output (MIMO) radar based on antennas and digital receivers and is weather-resilient. To make the technology applicable is needed to face the high costs of the high number of receivers. The EU-funded DEEP-RADAR project will apply a recently developed innovative method based on medical imaging technology that reduces the number of receivers while maintains high-resolution images. The project envisages the commercialisation of automotive MIMO radars.
Objective
The emerging autonomous vehicle ecosystem is expected to grow with an almost 40% CAGR in the next decade hitting €485 billion by 2026 and exceeding €6 trillion in 2050. There is wide industry consensus that improved long-range depth sensing modalities are imperative for the viability of self-driving cars. State-of-the-art optical technologies are still prohibitively expensive, have insufficient temporal and spatial resolution, do not provide an accurate velocity reading, and are restricted to a shorter range in adverse weather conditions. Millimeter wave multiple-input multiple-output (MIMO) radars are an attractive alternative relying on a phased array of transmitting antennas and digital receivers, containing no moving parts, and able to penetrate adverse weather conditions. The weakness of this technology is the costly requirement for a large number of receiver channels to achieve sufficient spatial resolution. We will apply our novel methodology recently developed for medical imaging to overcome this challenge.
We have demonstrated that learning the entire imaging pipeline in medical ultrasonography, including the shape of the transmitted pulses and the configuration of the receivers allows reducing the number of transmits by a factor of 3, while maintaining image quality comparable to traditional high-frame rate imaging protocols. Despite the different underlying physics, ultrasound and radar imaging share many conceptual similarities and have a similar mathematical description. Here, we intend to develop a proof-of-concept MIMO radar system demonstrating that by using the learned transmit patterns and image reconstruction pipeline, it is possible to halve the number of receive channels without compromising the image resolution and signal-to-noise ratio. Maintaining high resolution images using a smaller number of receiver channels will significantly reduce the cost of this technology and increase the commercial viability of automotive MIMO radars.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technologyradar
- natural sciencesbiological sciencesecologyecosystems
- engineering and technologymedical engineeringdiagnostic imaging
- natural sciencesphysical sciencesacousticsultrasound
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Programme(s)
Funding Scheme
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotHost institution
32000 Haifa
Israel