Creating digital road models of public test routes from kinetic LiDAR, followed by a live demonstration of the main features in rFpro for the testing, training and testing of Deep Learning Autonomous Driving:
i) real road models, not synthetic data - avoid patterns inherent in synthetic data that damage DNN training
ii) correlation with HD Maps - map the road signs detected by your perception algorithms to HD Map ground truth
iii) detail road surface for radar sensor models - localisation from road signature correlation
iv) Add traffic - Swarm and Programmed traffic
v) Add pedestrians - Swarm and programmed pedestrians, cyclists, animals etc
vi) Run real-time and add human test drivers - Mix stochastic, unpredictable, error-prone human behaviour live.
vii) Massively parallel, multiple ego cars, multiple sensor feeds, multiple human drivers