This project investigates abnormal driving situations, classifies them into a taxonomy and goes on to consider potential solutions for how to handle some of the more challenging automated driving scenarios.
The majority of the driving task is relatively routine, but occasionally situations demand the driver to take action which is out of the ordinary or requires the driver to make an interpretation of the situation and act in a considered manner (common sense driving). Such situations could present challenges to Automated Vehicles (AVs) and their developers. AVs will need to adhere to rules governing their behaviour. If the rules and regulations governing vehicle behaviour within abnormal situations are not clear, then this could lead to unexpected or undesirable behaviour amongst AVs. Indeed, AVs may behave differently to the same abnormal situation depending on the AV manufacturer and the software algorithms that have been deployed.
This study investigates how planning, designing, appraisal, implementation and operation of road infrastructure could adapt as a result of the introduction of CAVs.
Highways authorities, public bodies, developers and other organisations rely on planning and guidance material to guide future transport provision and investment priorities. Connected and Automated Vehicles (CAVs) have the potential to revolutionise transport, but many planning and guidance documents remain silent on the issue. In some cases, this is because the research that contributed to these documents pre-dates the technological progress that has been made in recent years in relation to CAVs. In other cases, there may be a reluctance to comment on a future which can appear to be unclear and rapidly changing. What is certain, however, is that the more we discuss the potential opportunities and issues that CAVs present, and the more strategies that are developed for maximising the benefits of them, the more likely it is that a positive outcome from their implementation can be achieved.
This award winning paper by TSC Technologist Carl Goves presents the results of applying an artificial neural network to estimate traffic conditions 15 minutes into the future on a section of motorway within the UK.