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Autonomous cars are an emergent technology which has the capacity to changehuman lives. The current sensor systems which are most capable of perceptionare based on optical sensors. For example, deep neural networks showoutstanding results in recognising objects when used to process data fromcameras and Light Detection And Ranging (LiDAR) sensors. However these sensorsperform poorly under adverse weather conditions such as rain, fog, and snow dueto the sensor wavelengths. This thesis explores new sensing developments basedon long wave polarised infrared (IR) imagery and imaging radar to recogniseobjects. First, we developed a methodology based on Stokes parameters usingpolarised infrared data to recognise vehicles using deep neural networks.Second, we explored the potential of using only the power spectrum captured bylow-THz radar sensors to perform object recognition in a controlled scenario.This latter work is based on a data-driven approach together with thedevelopment of a data augmentation method based on attenuation, range andspeckle noise. Last, we created a new large-scale dataset in the "wild" withmany different weather scenarios (sunny, overcast, night, fog, rain and snow)showing radar robustness to detect vehicles in adverse weather. High resolutionradar and polarised IR imagery, combined with a deep learning approach, areshown as a potential alternative to current automotive sensing systems based onvisible spectrum optical technology as they are more robust in severe weatherand adverse light conditions.

All-Weather Object Recognition Using Radar and Infrared Sensing