Leveraging Nash Equilibrium and Generative Adversarial Networks for Autonomous Driving
Autonomous driving in the context of Advanced Driver Assistance Systems (ADAS) and Highly Automated Driving (HAD) has evolved over the last couple of decades with an increasing significance towards Deep Learning principles from Artificial Intelligence. By combining this development with Nash Equilibrium, a concept from game theory, this paper discusses how we could establish and compute ground truths from vehicular videos used to train, test, and validate autonomous driving sensors and systems.
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