Sensor Fusion for Heavy Duty Vehicle Platooning
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Sensorfusion för tunga fordon i fordonståg (Swedish)
The aim of platooning is to enable several Heavy Duty Vehicles (HDVs) to drive in a convoy and act as one unit to decrease the fuel consumption. By introducing wireless communication and tight control, the distance between the HDVs can be decreased significantly. This implies a reduction of the air drag and consequently the fuel consumption for all the HDVs in the platoon.
The challenge in platooning is to keep the HDVs as close as possible to each other without endangering safety. Therefore, sensor fusion is necessary to get an accurate estimate of the relative distance and velocity, which is a pre-requisite for the controller.
This master thesis aims at developing a sensor fusion framework from on-board sensor information as well as other vehicles’ sensor information communicated over a WiFi link. The most important sensors are GPS, that gives a rough position of each HDV, and radar that provides relative distance for each pair of HDV’s in the platoon. A distributed solution is developed, where an Extended Kalman Filter (EKF) estimates the state of the whole platoon. The state vector includes position, velocity and length of each HDV, which is used in a Model Predictive Control (MPC). Furthermore, a method is discussed on how to handle vehicles outside the platoon and how various road surfaces can be managed.
This master thesis is a part of a project consisting of three parallel master’s theses. The other two master’s theses investigate and implement rough pre-processing of data, time synchronization and MPC associated with platooning.
It was found that the three implemented systems could reduce the average fuel consumption by 11.1 %.
Place, publisher, year, edition, pages
2012. , 56 p.
Extended Kalman Filter, EKF, platooning, sensor fusion
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-78970ISRN: LiTH-ISY-EX--12/4593--SEOAI: oai:DiVA.org:liu-78970DiVA: diva2:537306
Scania CV AB
Subject / course