Predictions and simulations of surrounding traffic for automated highway driving of long-combination vehicles

Abstract

Long combination vehicles (LCVs) are modular heavy trucks which can make the transport sector more effective. Due to the size and complex dynamics of these vehicles, automated driving (AD) functionality has the potential to improve traffic safety and prevent accidents. Lane changes on highways with very dense traffic is an example where traffic predictions are important in order for AD functionality to act safely. For LCV-sized vehicles, situations can occur where it simply is not possible to find a large enough gap in dense traffic which can accommodate the vehicle, and so communication with the surrounding traffic is necessary. This thesis examines the simulation of dense highway traffic situations where the traffic participants are able to react on intention signals such as turning indicators, as well as a method for predicting how the traffic situation will develop in the near future. This is done by introducing a concept of independent and dependent drivers in order to handle expected and emergency scenarios. Three highway traffic scenarios are identified for testing the functionality. It is shown that the system for automated driving becomes more risk-averse by considering the potential for emergency situations.