Thesis Work - Road Friction Adapted Vehicle Motion Planning

Thesis Worker at Volvo Cars
Welcome to explore the world of Volvo Cars by writing your thesis with us! As a thesis worker in our organization you are supported by a supervisor who follows you during your project. All thesis projects are arranged in business critical areas and therefore you will be able to contribute to our company purpose – providing freedom to move in a safe, sustainable and personal way – from day one!
 

About this opportunity - Background

Climate and accident data collected in Sweden from 1993 to 1997 showed that the risk of accidents increase 3-30 times when snow or ice is present compared to dry road. To tackle this safety problem with low friction, Volvo cars have spent more than 15 years of development and research into friction estimation. One major disadvantage is however that the estimator availability is low since the vehicle must either brake, accelerate or turn aggressively to enter the tire’s nonlinear region where the friction information is obtained. During everyday driving these types of maneuvers will not happen frequently. As a countermeasure, indirect methods have been developed using a front-facing camera together with machine learning. However, all estimation algorithms suffer from high estimate uncertainty, which makes it even more difficult to use in vehicle motion planning: a core functionality of advanced driver-assistance systems. Therefore, there is a need for developing methods for a safe motion planning under varying and uncertain road friction conditions.
 

As of today, even in-production driver-assistance active safety functions do not use any road condition information, which makes them potentially unsafe in low-friction conditions. For example, for the adaptive cruise control (ACC), the same distance to the vehicle ahead is maintained even if there is snow on the road, while a human driver would increase the distance for safety concerns. Similarly, an experienced driver would adapt the vehicle speed during heavy rain while cornering or changing lane to reduce accident risk. An autonomous function conducting such a maneuver is then expected to do the same.
 

Scope of the thesis work

The focus of this thesis is therefore to enhance the understanding of potential benefits of providing prior road friction information to the AD/ADAS systems on board. Uncertainty of the friction estimation will considerably influence the application possibility for vehicle motion planning and control. The translation framework from estimation uncertainty to reachability of vehicle motion will be investigated.

Expected deliverables:

• Quantify friction estimate uncertainty using potential camera-based method fusioned with model-based friction estimate commonly shared with cloud solution.
• Investigate possible AD/ADAS functions that would benefit from prior friction knowledge.
• Select 1 or 2 AD/ADAS functions and analyze the potential safety performance improvement using prior road friction information including uncertainty measures.
• Implement selected AD/ADAS functions in closed-loop simulation environment and evaluate the updated safety margins.
• Stretched target: if simulation results are promising and time permits, a real-time verification in a rapid prototyping test vehicle is available.

What you´ll bring

• Preferred background: Systems, Control, and Mechatronics or Complex adaptive systems
• On site with flexibility to work from home
• Experience using Simulink is a merit

Duration

• The work will start in January 2025, to be discussed 
• The duration for this thesis work is 20 weeks.
• 30 ECTS (academic credits) if in agreement with your Thesis Advisor in University
• This thesis is to be conducted by 2 Students working in pair.

Be part of the change – apply today!

Selection will be ongoing during the application period, so do not hesitate to send in your application. Attach your CV and personal letter stating your interests within the given area and your thoughts and credentials.

 Apply as soon as possible but no later than 2024-11-03

 Please note that applications via email will not be accepted.

If you have any additional questions regarding the thesis work, you are welcome to contact:

Supervisor, Ektor Karyotakis at ektor.karyotakis@volvocars.com

Manager, Anna Söderlund at anna.soderlund@volvocars.com

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