According to researchers from Deakin University, autonomous vehicles may be able to learn how to drive by watching humans. With the help of an improved sight-correcting system, self-driving cars could learn just by observing human operators complete a task. The team implemented imitation learning, also known as learning from demonstration. A human operator drives a vehicle outfitted with three cameras, which are observing the environment from the front and each side of the car. The data is then processed through a neural network — a computer system based on how the brain’s neurons interact to process information — that allows the vehicles to make decisions drawing on what it learned from watching the human make similar decisions. “The expectation of this process is to generate a model solely from the images taken by the cameras,” said Saeid Nahavandi, Alfred Deakin Professor, pro vice chancellor, chair of engineering, and director for the Institute for Intelligent Systems Research and Innovation at Deakin University. “The generated model is then expected to drive the car autonomously.” The processing system is a convolutional neural network, which is mirrored on the brain’s visual cortex. The network has an input layer, an output layer, and any number of processing layers between them. The input translates visual information into dots, which are then continuously compared as more visual information comes in. By reducing the visual information down to dots, the network is able to quickly process changes in the environment: A shift of dots appearing ahead could indicate an obstacle in the road. This, combined with the knowledge gained from observing the human operator, means the algorithm knows that a sudden obstacle in the road should trigger the vehicle to come to a complete stop to avoid an accident. “Having a reliable and robust vision is a mandatory requirement in autonomous vehicles, and convolutional neural networks are one of the most successful deep neural networks for image processing applications,” Nahavandi said. He did note a couple of drawbacks, however. One is that imitation learning speeds up the training process while reducing the amount of training data required to produce a good model. In contrast, convolutional neural networks require a significant amount of training data to find an optimal configuration of layers and filters, which can help organize data and produce a properly generated model capable of driving an autonomous vehicle. “For example, we found that increasing the number of filters does not necessarily result in a better performance,” Nahavandi said. “The optimal selection of parameters of the network and training procedure is still an open question that researchers are actively investigating worldwide.” Next, the researchers plan to study more intelligent and efficient techniques, including genetic and evolutionary algorithms to obtain the optimum set of parameters to better produce a self-learning, self-driving vehicle. The research was published in IEE/CAA Journal of Automatica Sinica (www.doi.org/10.1109/JAS.2019.1911825).