Industrial PhD Position in Lifelong Mapping for Autonomous vehicles

Scania is now undergoing a transformation from being a supplier of trucks, buses and engines to a supplier of complete and sustainable transport solutions.

Autonomous vehicle development at Scania is advancing at a very high pace and self-driving trucks and buses on public roads will soon be commonplace. Autonomous Transport Solutions (ATS) Research at Scania is responsible for developing, testing, and piloting future frontier autonomous driving concepts. 

Background  
HAD Maps or Maps for “Highly Autonomous Driving” is one of the core concepts in autonomous driving. They are used in almost every step of autonomous driving to varying extents. HAD Maps host spatially embedded information as layers that build upon one another. Reliable mapping augments the ability of a robot to perceive its surrounding environment, extends the virtual sensing range, assists in localizing the robot, and facilitates planning and predictions. The extensive use of HAD Maps necessitates that these maps truthfully represent the environment. HAD Maps start to degrade from the time of data collection. The work we intend to carry out in this Ph.D. project aims at addressing this problem area. 

Lifelong Mapping 
A lifelong mapping system is key for the long-term deployment of mobile robots in changing environments. The goal of this project is to identify the changes in the semantic information contained in the HAD map based on the robot's sensory observations. The data collected through the lidar and camera pipelines are matched and associated with the a-priori map information using machine learning techniques and statistical methods. The key idea of this project is to build a decision process to update the HAD map based on the data association process.  

The project provides the possibility to pursue a first-principles thinking approach, beginning with raw sensor measurements and designing a self-evolving system that can formulate interconnections between sensors and a-priori information in a data-driven fashion, while overcoming the shortcomings of the individual sensor modalities. 

To achieve these objectives, the industrial Ph.D. student will pursue multi-disciplinary research in the areas of statistical filtering, deep learning, graph theory, optimization, and sensing technologies. The work will be carried out in collaboration with Autonomous Transport Solutions (ATS) Research at Scania and the division for Robotics, Perception and Learning (RPL), under the supervision of Prof. Patric Jensfelt (academic supervisor) and Rohin Mohanadas (industrial supervisor).  

Your profile  
To apply, you should have a Master of Engineering degree (or equivalent) in applied mathematics, computer science, machine learning, robotics, engineering physics, electrical engineering or in a related technical science or engineering subject. We also welcome applicants that are currently completing the master thesis project. 

Mathematical skills in, e.g., optimization and stochastic processes, are desirable as are excellent programming skills. We look for candidates that are self-motivated, engaged, and can communicate in a pedagogic way to expert and non-expert audiences. You exhibit strong analytical skills, are well-organized and able to autonomously plan and execute tasks. You are fluent in English both in writing and in speech.   

Requirements  


• Solid theoretical knowledge of tracking, multi-sensor odometry, localization, mapping, SLAM, sensor fusion, signal processing, or machine learning 
• Experience with deep learning frameworks and architectures  
• Passion for coding and demonstrating theoretical results through running software in robotic platforms.

Extra meritorious if you have experience in one or more of the following: 


• Developing within a Linux-based development environment
• Solid Python, C++, ROS, and system-building skills
• Knowledge and experience within other programming languages and/or MATLAB/Simulink along with a keen interest in software development
• A proven interest in interdisciplinary research and record of initiative.  

Contact information   
For more information, you are welcome to contact Mansoureh Jesmani, EARD, +46855350359, mansoureh.jesmani@scania.com 

Application  
Apply with CV, cover letter and copies of your education certificate and transcript. The final application date is November 18, 2022. Interviews will take place continuously during the application period. As the position offers full-time employment at Scania, a background check might be conducted. 

IMPORTANT: APPLICATIONS WITHOUT TRANSCRIPTS OF RECORDS WILL NOT BE CONSIDERED.


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 2020, we delivered 66,900 trucks, 5,200 buses as well as 11,000 industrial and marine power systems to our customers. Net sales totalled to over SEK 125 billion, of which over 20 percent were services-related. Founded in 1891, Scania now operates in more than 100 countries and employs some 50,000 people. Research and development are mainly concentrated in Sweden. Production takes place in Europe and Latin America with regional product centres in Africa, Asia and Eurasia. Scania is part of TRATON GROUP. For more information visit: www.scania.com.