Automated prediction of road surface roughness using lidar data

What is our offer to you?
Imagine working with a diverse, fun, and passionate group of colleagues. Now imagine that you work with these colleagues on some of the most advanced autonomous vehicle technology in the world. Add that you are working in a culture that promotes growth, collaboration, trust and is striving to be the best autonomous solution verification and validation organization in the world! And having fun at the same time!


Are you ready to join our team as a thesis worker and be part of shaping the future of the transportation industry with us?


Who are we?
At Volvo Autonomous Solutions, we are passionate about the positive impact autonomous solutions will have on our society. We are on a journey to create something unique, developing innovative technologies and sustainable solutions that will change the future of transportation. With entirely new business models and advanced technology, we will meet and exceed expectations from customers and contribute to a society that we want to live in.


Background:
The road surface is the contact between vehicle and road. A vehicle has several wheels, and to know road surface, vertical height, and road roughness on each wheel is important for verification and validation of functions within Advanced Driver Assistance Systems (ADAS) and automated driving.


Road Roughness as deviation of a road surface from a designed surface grade influences safety conditions for road users. Therefore, being able to estimate road properties from sensor data is key to improve the safety of any vehicle.


Who are you?
We do not know you yet, but we believe that you have an interest in applied signal processing, sensor fusion, statistics and mathematics. Profiles of interest include Mathematics, Automation mechatronics, Control and signal processing. You are fluent in English, both written and spoken. Finally, it is meritorious if you have knowledge of Python and Robot Operating System (ROS).


What you will be doing:
The aim of this thesis is to develop a method to determine properties of the road surface from lidar data.
The work will include a literature study of state-of-the-art methods. You will collect and analyze data from automotive lidar sensors in order to identify and categorize relevant road data properties. Initial validation of the results should also be performed. In addition to this, you will document and report in English, as well as present the results.


Scope
• In vehicle LIDAR recordings
• Signal processing
• Road data property categorization
• Used language and technologies: Python, ROS


Thesis Level: Master
Language: English
Start date: January 2023
Number of students: 1 or 2


Tutors:
Lars Bjelkeflo, AD V&V Analytics Engineer, +46 31 322 6668
Stefan Thorn, AD V&V Analytics Engineer, +46 31 322 9768
Leo Laine, Innovation and Research Strategist, +46 31 323 53 11


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