30 credits – Vehicle motion planning with search trees and post-optimization

Scania genomgår nu en transformation från att vara en leverantör av lastbilar, bussar och motorer till en leverantör av kompletta och hållbara transportlösningar.

The Autonomous Vehicle Motion Planning and Control group at Scania develops the key components for local motion planning and control for our autonomous vehicles. A thesis project at the group and Scania is a great opportunity to work at the forefront of autonomous vehicle development, and an excellent way of making contacts for your future working life.


Motion planning is an essential part of the autonomous system, and aim to calculate trajectories for the vehicle to follow. These trajectories must meet the dynamical constraints of the vehicle, as well as the constraints from the surroundings. A common approach to planning the path between a start point and a goal point is to use a Rapidly exploring random tree (RRT). These are extensively used, but they tend to generate unnecessary long paths with excessive steering (“oscillatory”) behavior. Therefore, a lot of work has been put into creating more sophisticated versions of the RRT where these shortcomings are reduced, as well as post-optimization algorithms. This however, comes at the cost of increased complexity and computational time.


To develop an own, or suggest an existing, version of the RRT and path post-optimization algorithm.


To evaluate different versions of the RRT and path post-optimization algorithms, and to develop own algorithms. The algorithms shall be evaluated based on their performance, complexity and computational time, and recommendations shall be made for under what circumstances a certain algorithm is best suited. The algorithms shall be implemented and evaluated in simulation, and preferably also in a real vehicle.


Master (civilingenjör) in electrical engineering, physics, mechatronics, computer science or similar. Preferably with specialization in control theory, robotics or computer science.

Number of students: 1

Start date: January 2020

Estimated time needed: 20 weeks

Contact persons and supervisors:

Robin Andersson, Autonomous Vehicle Motion Planning and Control,

08 - 553 702 65


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