Large-scale deployment of (semi-)automated vehicles (AVs) is inevitable. However, the benefits of this deployment for traffic management in a world in which AVs and other vehicles will necessarily coexist (i.e., in ‘mixed’ traffic) remain unclear. Reduced congestion, greater energy efficiency, and improved resilience of the traffic system to unexpected events are expected. In this context, our hypothesis is that full advantage of large-scale deployment of AVs for traffic management will only be achieved if AVs and (the drivers of) conventional vehicles can coordinate their actions. This project will investigate how this might be enabled, for example, by developing and evaluating the impact of an advanced driver information system designed to provide drivers with advice on driving actions to be taken to optimise traffic flow in the presence of AVs.
School of Computer Science and Statistics