In English

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

Björn Persson Mattsson
Göteborg : Chalmers tekniska högskola, 2017. Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, ISSN 1652-8557, 2017.
[Examensarbete på avancerad nivå]

Long combination vehicles (LCVs) are modular heavy trucks which can make the transport sector more e ective. Due to the size and complex dynamics of these vehicles, automated driving (AD) functionality has the potential to improve trac safety and prevent accidents. Lane changes on highways with very dense trac is an example where trac 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 nd a large enough gap in dense trac which can accommodate the vehicle, and so communication with the surrounding trac is necessary. This thesis examines the simulation of dense highway trac situations where the trac participants are able to react on intention signals such as turning indicators, as well as a method for predicting how the trac 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 trac scenarios are identi ed for testing the functionality. It is shown that the system for automated driving becomes more risk-averse by considering the potential for emergency situations.

Nyckelord: Trac prediction, trac simulation, long combination vehicles, automated highway driving

Publikationen registrerades 2017-08-31.

CPL ID: 251550

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