Introduction to Autonomous Vehicles: Components, Architecture, Technologies, Operating Systems.
Localization: GNSS, LIDAR, Visual Odometry. Perception: Detection, Segmentation, Stereo, Optical flow, and
Scene flow, Tracking. Prediction & Routing: Planning and control, Traffic Prediction, Lane Level Routing.
Computer Vision Basics: Image formats, Edge detection, Convolution, Masking RoI, Corner detection,
Histograms, Feature extraction. Machine Learning: Linear and Logistic regression, SVMs and SVCs, Detecting
Cars using SVMs. Exercises using Deep Learning models and FPGAs: Line detection, Corner detection,
Vehicle speed determination, Traffic sign detection, Object recognition and tracking, Localization algorithm
implementation.