A platform for Sharing Artificial Intelligence Algorithms in Autonomous Driving : An overview of Enhanced LAOP

摘要

To solve the Autonomous Vehicle (AV) problem, an artificial learning model that translates sensor data into controls must be built. This way, the vehicle can react to its changing environment. To the best of our knowledge, there are no platforms where researchers can both develop new machine learning models, train them and compare them directly to others. We believe that such a platform can greatly impact the field of artificial intelligence and AV. In this paper, we propose an enhanced version of the Learning Algorithm Optimization Platform LAOP, called LAOP 2.0. It helps researchers in the field of artificial intelligence develop their models by allowing them to easily test and compare them. We also introduce the Learning Algorithm Sharing Platform (LASP), which makes it easy to share deep learning algorithms. As a demonstration of the versatility of our platform, we compared two learning algorithms. The first one, Fully Connected Neural Network (FUCONN), uses reinforcement learning to train itself. The second one, Mimicking HUman Behaviour (MHUB), uses supervised learning to adjust its weights and learns from human input. We demonstrate through extensive simulations that FUCONN outperforms MHUB after being trained.