Path Following using Frequency Modulated Continuous Wave millimetre-wave Automotive Radar

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Greisman, Austin
Radar , mmWave , Localization , Autonomous Vehicle , FMCW , Machine Learning , GRU
This thesis presents the discovery, methodology, and experimental validation of a path planning algorithm developed to only use Frequency Modulated Continuous Wave (FMCW) radar. Radar has shown to be semi-invariant to inclement weather such as snow, fog, extremely lighting conditions, and heavy rain. Traditionally, autonomous vehicle (AV) path planning algorithms use multiple introceptive and exteroceptive sensors to first determine the current position of the vehicle, then plan a path based on that position. These sensors include LiDAR, GNSS, cameras, inertial measurement units, ultrasonic, and radar. In previous research, the use of radar in these systems is to aid with the detection of obstacles, which could then be used to determine paths, not as a single modality system. This thesis presents research into using FMCW automotive radar with the aid of radar retro-reflectors to test the viability of a single modality path planning algorithm. The result is an efficient, Gated Recurrent Unit (GRU) deep neural network that has been trained on custom synthetically trained data of a vehicle following a path of retro-reflectors. The GRU network was then experimentally validated using a 60 kg skid-steer robot showing promising path planning results while in heavy fog. The results showed an average accuracy of 0.30 ± 0.15 m. When compared to LiDAR during the same testing conditions, the visual based sensor was unable to produce a viable path. As a result, this system satisfies the requirement for centimetre level accuracy for Level 3 autonomous driving in inclement weather.
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