Application of Machine Learning Algorithms for Post Processing of Reference Sensors

Abstract

The Autonomous Drive (AD) systems and Advanced Driver Assistance Systems (ADAS) in the current and future generations of vehicles include a large number of sensors which are used to perceive the vehicle’s surroundings. The production sensors of these vehicles are verified and validated against reference data that are originated from high-accurate reference sensors that are placed in a reference roof box at the top of the vehicle. In this thesis, are explored ways to strengthen the reference camera data by applying deep machine learning algorithms together with other techniques for 2D object detection. For this reason, they are used two driving related datasets, public Berkeley DeepDrive dataset (BDD100K) and Volvo’s annotated data. Also they are trained and evaluated two state of the art deep learning algorithms for object detection, Mask R-CNN [5] and YOLOv4 [25]. Finally, it is implemented in conjunction, a semi-supervised technique to improve the predictive performance using unlabeled data. The utilized semi-supervised learning framework is called STAC and it is introduced in the paper A Simple Semi-Supervised Learning Framework for Object Detection [27].