Publications

• Tarlan Suleymanov, “Route Boundary Inference with Vision and LiDAR,” PhD Thesis, University of Oxford, UK, 2019

• Tarlan Suleymanov, Matthew Gadd, Daniele De Martini, Paul Newman, “The Oxford Road Boundaries Dataset”, in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Workshop on 3D-Deep Learning for Automated Driving (3D-DLAD), (Nagoya, Japan), July 2021

• Tarlan Suleymanov, Paul Amayo, Paul Newman, “Inferring Road Boundaries Through and Despite Traffic,” in IEEE International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, 2018.

• Tarlan Suleymanov, Lars Kunze, and Paul Newman, “Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR,” in IEEE International Conference on Intelligent Transportation Systems (ITSC), Auckland, New Zealand, 2019.

• T. Suleymanov, L. M. Paz, P. Piniés, G. Hester, and P. Newman, “The Path Less Taken: A Fast Variational Approach for Scene Segmentation Used for Closed Loop Control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016.

• T. Suleymanov, M. Gadd, L. Kunze, and P. Newman, “LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic,” in IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, 2020.

• L. Kunze, T. Bruls, T. Suleymanov, and P. Newman, “Reading between the Lanes: Road Layout Reconstruction from Partially Segmented Scenes,” in IEEE International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, 2018.

• L. M. Paz, T. Suleymanov, P. Piniés, G. Hester, and P. Newman, “On-line Scene Understanding for Closed Loop Control,” in Robotics: Science and Systems, Workshop on Geometry and Beyond: Representations, Physics, and Scene Understanding for Robotics, 2016.

• L. Kunze, T. Bruls, T. Suleymanov, and P. Newman, “Reading between the Lanes: Road Layout Reconstruction from Partially Segmented Scenes (Extended Abstract),” in Robotics Science and Systems (RSS) Workshop on Learning and Inference in Robotics: Integrating Structure, Priors and Models, 2018.

Oxford Road Boundaries Dataset

• https://oxford-robotics-institute.github.io/road-boundaries-dataset/

Patents

• Suleymanov, Tarlan. A computer-implemented method of generating training data for training a machine learning model. WO2024018172A1. Google Patents, https://patents.google.com/patent/WO2024018172A1

• Suleymanov, Tarlan. A computer-implemented method of generating a lane boundary model of a route traversed by an autonomous vehicle. WO2024018173A1. Google Patents, https://patents.google.com/patent/WO2024018173A1

• WO2025062115A1, https://patents.google.com/patent/WO2025062115A1

• WO2025062116A1, https://patents.google.com/patent/WO2025062116A1

• GB2633629A, https://patents.google.com/patent/GB2633629A

• GB2633630A, https://patents.google.com/patent/GB2633630A

Press

• Ən yaxşı innovativ layihənin qalibi, https://edu.gov.az/az/news-and-updates/9681

Talks & seminars

• "Sürücüsüz avtomobillər və onların tətbiqi", plenary talk, XXVII Republican Scientific Conference of Doctoral Students and Young Researchers (NASCO XXVII), Sumgayit State University, 10 December 2024

• "Computer Vision with Deep Learning", lecture series, Startup School 2 - AI for Green Solidarity, Center for Analysis of Economic Reforms and Communication, 3 lectures on 17, 24, and 27 May 2024.

• "AI and Future", panel session, Artificial Intelligence in Digital Governance (AIFORDIGIGOV), E-GOV Development Center of the State Agency for Public Service and Social Innovations, 26 April 2021

• "Sürücüsüz avtomobillər və təhsil-sənaye əməkdaşlığı", webinar, Institute of Education of the Republic of Azerbaijan, 3 March 2021

• "TOP-100 universitetdə təhsil", panel session, Azerbaijan Science Association, 5 February 2021

• "Süni intellekt: insanlıq üçün ümid yoxsa təhlükə?", panel session, Azerbaijan Science Association, 5 June 2020

• "Sürücüsüz avtomobillər", seminar, Khazar University, 13 October 2017

• "Sürücüsüz avtomobillər", seminar, Multidisciplinary Science Forum, 8 October 2017