Dr.ir. S.C. (Simeon) Calvert

Dr.ir. S.C. (Simeon) Calvert

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Room: 23.HG 4.02.2

Profile

Dr. Simeon Calvert is associate professor of Smart & Automated Driving in the department of Transport & Planning at the TU Delft. He is director and founder of the Automated Driving & Simulation (ADaS) research lab and co-director of the CityAI-lab for research on urban behaviour using AI. He is also a board member of the Centre for Meaningful Human Control over AI, the interdisciplinary research program for responsible autonomous technology. His research is focussed on the impacts of technology on road traffic through experimentation, conceptualization and simulation.   

Expertise

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Prizes

  • 2023-9-28

    Best Student Paper Runner-up Award (IEEE ITSC 2023)

    Won the Best Student Runner-up Award at 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
    26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023

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  • 2020-7-29

    Certificate of appreciation

    Awarded by the Vehicle-Highway Automation Committee at the 2021 TRB Annual Meeting with appreciation and respect for great work as an organizer of the Automated Vehicles Symposium 2020 ~ July 27–30, 2020
    100th Annual Meeting of the Transportation Research Board (TRB)

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  • 2016-12-16

    Best paper award ACSEE 2016

    Best paper award at International Conference on Advances in Civil, Structural and Environmental Engineering - ACSEE 2016, for paper 'Considering knowledge gaps for automated driving in conventional traffic'
    http://www.traffic-quest.nl/images/stories/Plaatjes/Fotos/ascee_2.jpg
    4th International Conference on Advances in Civil, Structural and Environmental Engineering

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  • 2015

    Greenshields Prize 2015

    This paper proposes a real time travel time prediction framework designed for large urban area including both arterial and urban roads. This framework makes it possible to test a wide variety of prediction models based either on theoretical or data-driven approaches. The results are demonstrated in a large test case corresponding to the Amsterdam Practical Trial. Data-driven approaches were then favor because their are easier to calibrate and require less computations. For short-term prediction, it appears that the simplest data driven approach (naive approach) performs the best. For larger-time window, a refined method (historic median prediction) provides the more accurate results. In most cases, the average absolute relative error is below 20%. The main contributions of this paper are (i) the formulation of the global framework and (ii) the extensive test of different methods on a large and heterogeneous operational test cases. The operational feedbacks from this study provide a good state of the art of the performance of data-driven methods in a mixed context and pave the way of further methodological developments

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Room: 23.HG 4.02.2