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Scania is a world-leading provider of transport solutions. Together with our partners and customers we are driving the shift towards a sustainable transport system. In 2017, we delivered 82 500 trucks, 8 300 buses as well as 8 500 industrial and marine engines to our customers. Net sales totalled nearly SEK 120 billion, of which about 20 percent were services-related. Founded in 1891, Scania now operates in more than 100 countries and employs some 49 000 people. Research and development are concentrated in Sweden, with branches in Brazil and India. Production takes place in Europe, Latin America and Asia, with regional production centres in Africa, Asia and Eurasia. Scania is part of Traton Group. For more information visit www.scania.com.
Scania is now undergoing a transformation from being a supplier of trucks, buses and engines to a supplier of complete and sustainable transport solutions.
The Autonomous Vehicle Sensors group at Scania is responsible for sensors and develops sensor pre-treatment software for autonomous trucks. A thesis project at the group and Scania is a great opportunity to work on the forefront of autonomous vehicle development and an excellent way of making contacts for your future working life.
Background:
Autonomous vehicle development is advancing at a very high pace and self-driving trucks on public roads will soon see the light of day. In the Autonomous Vehicle Sensor group we are responsible for sensors and develops sensor pre-treatment software. Our systems are essential in enabling high level autonomy for the transportation systems of the future. Modern sensor technologies such as LIDAR, RADAR and Cameras are used to get a detailed picture of the environment surrounding the truck. To get the correct environmental perception, it is crucial to know the sensor performance. The autonomous vehicle must, without human interaction, be able to determine the sensor performance. The sensor performance can for example be described in how reliable the sensor is at a certain range. The range of a sensor is not only dependent on the sensor itself, the range is greatly dependent on occlusion from environmental conditions such as road and land curvature that can be obtained from map information, weather conditions, soiling, static objects and dynamic objects.
Target:
To develop an approach for detecting the performance of a sensor, what is the range of the sensor in the current environmental condition and what is the reliability of the sensor data.
Assignment:
Find a strategy for using raw sensor data and/or sensor fusion to monitor the performance of an individual sensor based on state of the art methods such as machine learning. The algorithm should also be implemented and tested, either in simulations or in real experiments.
Education:
Master (civilingenjör) in electrical engineering, physics, mechatronics, or similar, preferably with specialization in control, robotics, machine learning or artificial intelligence.
Number of students: 1-2 students
Start date: Spring 2019 by agreement, earliest February 2019
Estimated time needed: 20 weeks
Contact persons and supervisors:
Mattias Johansson, Development engineer at Autonomous Vehicle Sensors,
08 – 553 803 21,
Enclose CV, coverletter and transcript of records
18-03-2024
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