The research group is working on research, development, and implementation of autonomous vehicles, industrial mobile robotics, and smart city testbed. In particular, we work on autonomous driving technologies for self-driving vehicles, simulations and validations in terms of safety, usability and edge case detection, mapping and virtual environment creation based on lidar and camera data from UAVs, smart city testbed development and user experiments in the future urban environment.
The main objective is to research and develop self-driving vehicles and autonomous mobile robots focused on automating transportation and delivery tasks by autonomously transporting and delivering people, materials, and products in urban and industrial environments safely and reliably.
The broader vision and objective of autonomous vehicle research and development is to introduce a small-scale future city environment into the university campus, where autonomous vehicles are in focus. Whether these vehicles are small package transfer robots, mid-size autonomous agricultural vehicles or full-scale self-driving cars. The main research activity in the autonomous vehicles lab is developing a last-mile vehicle in cooperation with the industry. As a result of the project, a last-mile AV shuttle is built from scratch and is used for research and education purposes for the whole university, as well as targeted for commercialization for the company. The vehicle is fully autonomous and is built on open-source software solutions.
Research partners and organizations
Advanced Mobility Institute at Florida Polytechnic University
Florida Poly’s Advanced Mobility Institute (AMI) is a university-affiliated research centre focused on advancing and testing AV technology. AMI is the only of its kind in the state and is but one of the largest university centres specialized in the narrow area of testing and verification of AV technology in the country.
The agreement between Florida Poly and TalTech furthers the development of AV technology within downtowns and university campuses, as well as other controlled settings where driverless technology can be better controlled. It also creates work and study opportunities to support the implementation and testing of this technology, along with promoting education and outreach efforts regarding its applications.
The Autoware Foundation (AWF), the open alliance for autonomous driving technology, welcomed 8 companies in the past quarter from all over the world, including Taiwan, Japan, India, Estonia and the US. Since the establishment of the AWF in December 2018, the AWF has made significant efforts to build a genuinely open alliance community for self-driving intelligent mobility for everyone. With our mission – initiate, grow, and fund Autoware projects – in mind, we will continue our exciting journey with 40 members.
“Autoware is a true leader in open-source autonomous driving software, and TalTech is excited to join Autoware Foundation. Thanks to Autoware, our joint team were able to build a Level 4 AV shuttle ISEAUTO in one year – an achievement which would never be possible without Autoware. Our team, academic staff, and students are eager to contribute to Autoware and ROS2 development to make autonomous driving smarter and safer. The collaboration matters most, and the open-source automotive solution is the best way to go.”
— Raivo Sell, Head of Autonomous Vehicles Lab, Tallinn University of Technology (TalTech)
The International Alliance for Mobility Testing & Standardization (IAMTS)
IAMTS is an SAE ITC (Industry Technologies Consortia) program. The SAE ITC team specializes in establishing and managing consortia by providing proven processes, tools and resources. ITC enables public, private, academic and government organizations to connect and collaborate in neutral, pre-competitive forums, thus empowering the setting and implementation of strategic business improvements in highly engineered industries globally.
Open Ph.D position
Currently no open position
Open post-doc position
Title: Self-driving vehicle behaviour in complex urban environments
The research focuses on the following general topics:
- Autonomous driving algorithms
- Decision-making based on fused sensor input
- Co-operational behaviour based on V2V and V2X
- Ph.D in Robotics, Mechatronics, ICT or a similar engineering program
- Experience in programming C/C++
- Experience with ROS
- Good English and communication skills
- Simulation tools
- Electrical engineering
Please send your motivation letter and CV to email@example.com
Guest researchers and fellows
We are accepting researchers, Ph.D and master students to work in our lab for short or long-term visits. Erasmus and exchange students are welcome, as well as post-docs and semester project master students.
In the case of interest, please study first our ongoing projects and research background.
- Basic knowledge of mechanical engineering, electronics and software development
- Advanced knowledge of electrical engineering or computer science
- Practical experience with ROS, ROS2, or embedded systems
Malayjerdi, Ehsan; Sell, Raivo; Malayjerdi, Mohsen; Udal, Andres; Bellone, Mauro (2022). Practical path planning techniques in overtaking for autonomous shuttles. Journal of Field Robotics, 39 (4), 410−425. DOI: 10.1002/rob.22057.
Pikner, H.; Sell, R.; Majak, J.; Karjust, K. (2022). Safety System Assessment Case Study of Automated Vehicle Shuttle. Electronics, 11 (7), 1162. DOI: 10.3390/electronics11071162.
Gu, J.; Bellone, M.; Sell, R.; Lind, A. (2022). Object Segmentation for Autonomous Driving Using iseAuto Data. Electronics, 11 (7), #1119. DOI: 10.3390/electronics11071119.
Kalda, K.; Pizzagalli, S.-L.; Soe, R.-M.; Sell, R.; Bellone, M. (2022). Language of Driving for Autonomous Vehicles. Applied Sciences, 12 (11), #5406. DOI: 10.3390/app12115406.
Sell, R.; Soe, R.-M.; Wang, R.; Rassõlkin, A. (2021). Autonomous Vehicle Shuttle in Smart City Testbed. In: Zachäus C., Meyer G. (Ed.). Intelligent System Solutions for Auto Mobility and Beyond. (143−157). Springer, Cham. (Lecture Notes in Mobility). DOI: 10.1007/978-3-030-65871-7_11.
Caltagirone, L.; Bellone, M.; Svensson, L.; Wahde, M.; Sell, R. (2021). Lidar-Camera Semi-Supervised Learning for Semantic Segmentation. Sensors, 21 (14), #4813. DOI: 10.3390/s21144813.
Sell, R.; Pikner, H.; Majak, J.; Karjust, K. (2021). Multi-layer cyber-physical control method for mobile robot safety systems. Proceedings of the Estonian Academy of Sciences, 70 (4), 383−391. DOI: 10.3176/proc.2021.4.03.
Sell, R.; Malayjerdi, M.; Malayjerdi, E.; Baykara, B. C. (2021). Autonomous vehicle safety evaluation through a high-fidelity simulation approach. Proceedings of the Estonian Academy of Sciences, 70 (4), 413−421. DOI: 10.3176/proc.2021.4.07.
Kalda, K.; Sell, R.; Soe, R. M. (2021). Use case of Autonomous Vehicle shuttle and passenger acceptance analysis. Proceedings of the Estonian Academy of Sciences, 70 (4), 429−435. DOI: 10.3176/proc.2021.4.09.
Wang, R.; Sell, R.; Rassõlkin, A.; Otto, T.; Malayjerdi, E. (2020). Intelligent Functions Development on Autonomous Electric Vehicle Platform. Journal of Machine Engineering, 20 (2), 114−125. DOI: 10.36897/jme/117787.
Udal, Andres; Jürise, Martin; Kaugerand, Jaanus; Sell, Raivo (2020). COMSPECT: A compact model for green vegetation reflection spectra in 400 – 900 nm wavelength range. Proceedings of the Estonian Academy of Sciences, 69 (4). 10.3176/proc.2020.4.01
Wang, R.; Sell, R.; Rassõlkin, A.; Otto, T.; Malayjerdi, E. (2020). Intelligent Functions Development on Autonomous Electric Vehicle Platform. Journal of the Machine Engineering, 20 (2), 114−125.10.36897/jme/117787.
Sell, R.; Leier, M.; Rassõlkin, A.; Ernits, J.-P. (2020). Autonomous Last Mile Shuttle ISEAUTO for Education and Research. International Journal of Artificial Intelligence and Machine Learning, 10 (1), 18−30.10.4018/IJAIML.2020010102.
Sell, R.; Rassõlkin, A.; Wang, R.; Otto, T. (2019). Integration of autonomous vehicles and Industry 4.0. Proceedings of the Estonian Academy of Sciences, 68 (4), 389−394. 10.3176/proc.2019.4.07.
Sell, R.; Väljaots, E.; Pataraia, T.; Malayjerdi, E. (2019). Modular smart control system architecture for the mobile robot platform. Proceedings of the Estonian Academy of Sciences, 68 (4), 395−400. 10.3176/proc.2019.4.08.
Väljaots, E.; Sell, R. (2019). Energy efficiency profiles for unmanned ground vehicles. Proceedings of the Estonian Academy of Sciences, 68 (1), 55−65.10.3176/proc.2019.1.04.
Rassõlkin, A.; Sell, R.; Leier, M. (2018). Development case study of the first Estonian self-driving car, ISEAUTO. Electrical, Control and Communication Engineering, 14 (1), 81−88.10.2478/ecce-2018-0009.
Sell, Raivo; Petritsenko, Andres (2013). Early design and simulation toolkit for mobile robot platforms. International Journal of Product Development, 18, 2, 168−192.10.1504/IJPD.2013.053499.
Sell, Raivo; Seiler, Sven (2012). Improvements of Multi-disciplinary Engineering Study by Exploiting Design-centric Approach, Supported by Remote and Virtual Labs. International Journal of Engineering Education, 28 (4), 759−766.
Sell, Raivo; Leomar, Priit (2010). Universal Navigation Algorithm Planning Platform for Unmanned Systems. Solid State Phenomena, 164, 405−410. 10.4028/www.scientific.net/SSP.164.405
SAE Edge Report contribution
Razdan,R.; Sell,R.; Tino,C.; Schmidt,B.; Akbas,M.; Cherian,J.; Boer,N. (2020).
Unsettled Topics Concerning Autonomous Public Transportation Systems. SAE Technical Paper Series
Razdan, R.; Mahoney, W.; Haritan, E.; Kalia, A.; Smith, J.; Zarola, T.; Akbas, I.; Taiber, J.; Straub, E.; Sell, R. (2020).
Unsettled Topics Concerning Automated Driving Systems and the Development Ecosystem. SAE Technical Paper Series.
Razdan, R.; Lumina, J.; Balachandran, A.; Cheng, C.; Sreenivas, S.; Fernando, X.; Taiber, J.; Kalia, A.; Keel, N.; Zuby, D.; Krishnan, K.; Langer, D.; Sell, R. (2019).
Unsettled Technology Areas in Autonomous Vehicle Test and Validation. SAE Technical Paper Series.10.4271/EPR2019001.
Taiber, J.; Sell, R.; Shorten,R.; Kraus, A.; Baxter, D.; Grosse, R.; Pfliegl, R.; Martin,J.; Yager, M.; Kelley, S.; Kramer, H.; Bonomi, F. (2019).
Unsettled Impacts of Integrating Automated Electric Vehicles into a Mobility-as-a-Service Ecosystem and Effects on Traditional Transportation and Ownership. SAE Technical Paper Series.10.4271/EPR2019004.
Raivo Sell ORCID iD https://orcid.org/0000-0003-1409-0206