Title
SafeVRU: A research platform for the interaction of self-driving vehicles with vulnerable road users
Author
Ferranti, L. (TU Delft Intelligent Vehicles) ![ORCID 0000-0003-3856-6221 ORCID 0000-0003-3856-6221](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Ferreira de Brito, B.F. (TU Delft Learning & Autonomous Control) ![ORCID 0000-0003-3598-8015 ORCID 0000-0003-3598-8015](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Pool, E.A.I. (TU Delft Intelligent Vehicles) ![ORCID 0000-0002-4841-2093 ORCID 0000-0002-4841-2093](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Zheng, Y. (TU Delft Intelligent Vehicles)
Ensing, R.M. (TU Delft Intelligent Vehicles)
Happee, R. (TU Delft Intelligent Vehicles) ![ORCID 0000-0001-5878-3472 ORCID 0000-0001-5878-3472](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Shyrokau, B. (TU Delft Intelligent Vehicles) ![ORCID 0000-0003-4530-8853 ORCID 0000-0003-4530-8853](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Kooij, J.F.P. (TU Delft Intelligent Vehicles) ![ORCID 0000-0001-9919-0710 ORCID 0000-0001-9919-0710](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control) ![ORCID 0000-0003-0058-570X ORCID 0000-0003-0058-570X](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Gavrila, D. (TU Delft Intelligent Vehicles) ![ORCID 0000-0002-1810-4196 ORCID 0000-0002-1810-4196](/sites/all/themes/tud_repo3/img/icons/orcid_16x16.png)
Date
2019
Abstract
This paper presents our research platform SafeVRU for the interaction of self-driving vehicles with Vulnerable Road Users (VRUs, i.e., pedestrians and cyclists). The paper details the design (implemented with a modular structure within ROS) of the full stack of vehicle localization, environment perception, motion planning, and control, with emphasis on the environment perception and planning modules. The environment perception detects the VRUs using a stereo camera and predicts their paths with Dynamic Bayesian Networks (DBNs), which can account for switching dynamics. The motion planner is based on model predictive contouring control (MPCC) and takes into account vehicle dynamics, control objectives (e.g., desired speed), and perceived environment (i.e., the predicted VRU paths with behavioral uncertainties) over a certain time horizon. We present simulation and real-world results to illustrate the ability of our vehicle to plan and execute collision-free trajectories in the presence of VRUs.
To reference this document use:
http://resolver.tudelft.nl/uuid:212d35d0-792f-4c7b-b4d9-fa6cf8ff5afb
DOI
https://doi.org/10.1109/IVS.2019.8813899
Publisher
IEEE, Piscataway, NJ, USA
Embargo date
2019-03-01
ISBN
978-1-7281-0560-4
Source
Proceedings IEEE Symposium Intelligent Vehicles (IV 2019)
Event
IEEE Intelligent Vehicles Symposium 2019, 2019-06-09 → 2019-06-12, Paris, France
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2019 L. Ferranti, B.F. Ferreira de Brito, E.A.I. Pool, Y. Zheng, R.M. Ensing, R. Happee, B. Shyrokau, J.F.P. Kooij, J. Alonso-Mora, D. Gavrila