Master Thesis Student

Adaptive Autosar for motion control of autonomous heavy vehicles
Background
Motion control for autonomous vehicles is used to control the driving dynamics of the whole vehicle. It has the task to control all the available actuators in a vehicle to safely and efficiently follow the intended path along the road. Many of the software algorithms used have properties that traditionally have been well suited to implement on a Classic Autosar or similar platform, and the whole system is designed to utilize such a platform in the best way. However, as these types of systems are growing and features such as dynamic configuration of the software are needed, platforms more similar to desktop or mobile systems might be needed. Adaptive Autosar is an example of such a platform and is being adopted within the automotive industry. More information about Autosar is found at https://www.autosar.org/.
Objective
The objective of the thesis is to investigate the suitability to move to an Adaptive Autosar platform, for a system that is mainly responsible for motion control. The main motivation for such a move is to take advantage of the flexibility while not compromising on the overall determinism and safety of the system. Which parts of the system that is suitable and which are not is to be identified. The parts that are suitable should be ported to on an Adaptive Autosar platform as proof of concept. How a split into both Classic Autosar and Adaptive Autosar impacts the complete system, including the development environment, should be evaluated. Particular focus when evaluating the development environment should be on the suitability of using model based tools such as Simulink.
Scope and Method
Firstly, a literature survey about the state of the art usage of Adaptive Autosar should be conducted. Particular focus should be on the suitability for motion control type of systems. After this first step, the Volvo system should be analyzed and candidates for moving to Adaptive Autosar should be identified. These candidates should then be ported to Adaptive Autosar. Finally the impact on the complete system and its development environment should be evaluated.
Suitable background: Computer science or similar, with an interest for embedded systems.
Thesis Level: Master
Language: English
Starting date: January 2020
Number of students: 1-2
Tutor: Christoffer Markusson, +46 73 902 68 83

会社概要

The Volvo Group is one of the world’s leading manufacturers of trucks, buses, construction equipment and marine and industrial engines under the leading brands Volvo, Renault Trucks, Mack, UD Trucks, Eicher, SDLG, Terex Trucks, Prevost, Nova Bus, UD Bus and Volvo Penta.

Volvo Group Trucks Technology provides Volvo Group Trucks and Business Area's with state-of-the-art research, cutting-edge engineering, product planning and purchasing services, as well as aftermarket product support. With Volvo Group Trucks Technology you will be part of a global and diverse team of highly skilled professionals who work with passion, trust each other and embrace change to stay ahead. We make our customers win.

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