30 credits – Speed Planning for Autonomous Heavy-Duty Vehicles

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Ingress:

A thesis project at Scania is an excellent way of making contacts for your future working life. Many of our current employees started their career with a thesis project.

Background:

The technical development of autonomous driving technologies is progressing rapidly, and fully autonomous vehicles will soon have a place in our everyday life. In order to drive, autonomous vehicles need to plan velocity profiles, that when associated with a path to be performed, will result in safe and time-optimal maneuvers.

Dubins paths correspond to the absolute shortest path between two vehicle poses. Even though they are the shortest path, this does not guarantee that they are time-optimal. In fact, speed planning approaches must be implemented such in order to ensure that the vehicle performs the path as fast as possible, while complying with the vehicle engine, friction, and rollover limits.

Implementing an appropriate speed planning method will result in reduced execution times, increasing the efficiency of processes making using of heavy duty vehicles. Furthermore by taking into account vehicle limitations, it is possible to increase the safety and prevent accidents involving heavy duty vehicles.

Target:

To understand and identify the challenges that heavy duty vehicle drivers face when performing maneuvers, and how can speed profiles be adapted in order to take them into account.

To study and develop algorithms for speed planning for Dubins paths. A study is to be made into different alternatives, which can be rule-based, numerical optimization-based or following any other suitable methods.

Assignment:

To get an understanding of the current tools for speed planning, and how they can be applied to heavy duty vehicles.

To get an understanding of the physical limitations affecting heavy duty vehicles, such as friction and rollover constraints.

To develop a speed planning algorithm for Dubins paths, which takes into account the physical limitations.

To generalize the speed planning algorithm for more arbitrary paths.

Education:

Master (civilingenjör) in engineering physics, electrical engineering, computer science, or applied mathematics.

Number of students: 1-2

Start date: January 2019

Estimated time needed: 20 weeks

Contact persons and supervisors:

Rui Oliveira, Industrial PhD in Autonomous Motion, rui.oliveira@scania.com, 08 – 553 719 54

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