New sparse RNN architecture applied to autonomous vehicle control

Researchers from TU Wien, IST Austria and MIT have developed a recurrent neural network (RNN) method for application to specific tasks within an autonomous vehicle control system. What is interesting about this architecture is that it uses just a small number of neurons. This smaller scale allows for a greater level of generalization and interpretability compared with systems containing orders of magnitude more neurons. The researchers found that a single algorithm with 19 control neurons, connecting 32 encapsulated input features to outputs by 253 synapses, learnt to map high-dimensional inputs into steering commands. This was achieved by use of a liquid time-constant RNN, a concept that they introduced in 2018.