In this project we develop and deploy quantum computing software for autonomous vehicles, with the aim of supporting the control system in its decision making process. The aim of this project is to develop an end-to-end control system deployed in cars, where quantum computers are used to enhance the decision-making process in the control system. Autonomous systems need to repeatedly take decisions as to whether they should take a specific action or not. This is a difficult challenge, particularly when the input from different sensor data is considered. For example, deciding whether a lane change is safe is relatively straight forward for humans, but is difficult for automated control systems. QCs process data in an inherently parallel way, with a possibilistic outcome of the measurements. These can provide complementary information to the control system and hence enhance its decision-making capabilities.
To this aim we use so-called Quantum Neural Networks as part of a Quantum Machine Learning approach to predict the safety of specific autonomous car manoeuvres. The decision depends on the positions and velocities of multiple surrounding cars. Quantum Neural Networks have been shown to train faster than classical models for certain cases, and hence have the potential to outperform classical machine learning algorithms used in the autonomous vehicle industry.
This is an InnovateUK project.