In the case of commercial vehicles fuel consumption is an important factor. It has a significant impact both on delivery prices and on the ecological impact of shipping goods. Hence making fuel-efficient engines is a main priority for truck manufacturers. Moreover they not just optimize engines, but even driver assistant functions have been developed in order to further reduce the amount of fuel needed. As autonomous vehicles become more and more widespread, logistic companies will likely utilize these vehicles in their fleet. These autonomous vehicles can be more fuel-efficient and eco friendly than the ones driven by human drivers. This can be achieved by using data unavailable to humans, and by doing computations a person could not possibly do.
A significant method for large scale transportation is shipping by train. Engineers working on reducing costs associated with train transporting came up with the idea of utilizing topological data of the rail tracks. The main idea of theirs was that once the train climbed up on a hill, it will roll down due to gravity.
During this period driving is unnecessary. This idea should also be working with trucks delivering on the highways.
This thesis shows how such a Predictive Cruise Control function may be made and test how it performs.
As a part of my work I made an Android application that can plan the optimal speed profile along a given route, and send the currently optimal speed to the truck based on that. Furthermore on the truck side, I made a simple controller that can control the vehicle speed based on the received information. During the test, I compared this function with the original speed governing function built-in the truck. The amount of saved fuel didn't reach a significant amount, but I made sure that the function fulfills all other targeted goals.