Autonomous systems such as self-driving cars are increasingly relying on LIDAR systems for robust environmental perception, with single-photon avalanche diode (SPAD) sensors, which detect and time individual photons of light, being one of the key underlying technologies. In recent years, large array format SPADs have emerged, with integrated digital processing to provide 3D imaging in a compact form factor. However, challenges remain in the detection and classification of long-range objects, especially under high ambient light levels.
This project aims to develop a single-photon LIDAR system that operates at multiple wavelengths, as opposed to conventional single-wavelength systems, and as such provides enhanced perception and ambient light suppression. To this end, system-level integration will be undertaken of existing SPAD arrays with suitable optics and customised laser sources. A microcontroller or FPGA will be used to interface to a computer for real-time acquisition. There will also be a focus on developing neural network models for enhanced object detection, tailored to the multispectral single-photon data. In addition to LIDAR, there will be opportunities to explore life sciences applications of the resulting imaging system.
The project is funded by Sony and will involve close engagement with Sony Europe Technology Development Center (EUTDC) in Trento, Italy, with the results of the project potentially informing the design of future SPAD sensors. The project would suit candidates with a background in Electronics/Computer Science and strong interest in image sensor technology and AI-based image processing, as well as a readiness to conduct physical experiments.
Please note, the position will be filled once a suitable candidate has been identified.
 O. Kumagai et al., (2021), A 189×600 Back-Illuminated Stacked SPAD Direct Time-of-Flight Depth Sensor for Automotive LiDAR Systems, ISSCC 2021, https://doi.org/10.1109/ISSCC42613.2021.9365961
 Taher J et al. (2022), Feasibility of Hyperspectral Single Photon Lidar for Robust Autonomous Vehicle Perception. Sensors, 22(15). https://doi.org/10.3390/s22155759
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