  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.

Computer Vision and Pattern Recognition;Robotics