Autonomous cars are an emergent technology which has the capacity to change human lives. The current sensor systems which are most capable of perception are based on
optical sensors. For example, deep neural networks show outstanding results in recognising objects when used to process data from cameras and Light Detection And Ranging
(LiDAR) sensors. However these sensors perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This thesis explores new
sensing developments based on long wave polarised infrared (IR) imagery and imaging
radar to recognise objects. First, we developed a methodology based on Stokes parameters
using polarised infrared data to recognise vehicles using deep neural networks. Second,
we explored the potential of using only the power spectrum captured by low-THz radar
sensors to perform object recognition in a controlled scenario. This latter work is based
on a data-driven approach together with the development of a data augmentation method
based on attenuation, range and speckle noise. Last, we created a new large-scale dataset
in the ”wild” with many different weather scenarios (sunny, overcast, night, fog, rain and
snow) showing radar robustness to detect vehicles in adverse weather. High resolution
radar and polarised IR imagery, combined with a deep learning approach, are shown as a
potential alternative to current automotive sensing systems based on visible spectrum optical technology as they are more robust in severe weather and adverse light conditions.UK Engineering and Physical Research Council, grant reference EP/N012402/
