A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation

Fuente: arXiv
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Hauptverfasser: de Groot, Oscar, Bertipaglia, Alberto, Boekema, Hidde, Jain, Vishrut, Kegl, Marcell, Kotian, Varun, Lentsch, Ted, Lin, Yancong, Messiou, Chrysovalanto, Schippers, Emma, Tajdari, Farzam, Wang, Shiming, Xia, Zimin, Zaffar, Mubariz, Ensing, Ronald, Garzon, Mario, Alonso-Mora, Javier, Caesar, Holger, Ferranti, Laura, Happee, Riender, Kooij, Julian F. P., Papaioannou, Georgios, Shyrokau, Barys, Gavrila, Dariu M.
Format: Preprint
Veröffentlicht: 2025
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author de Groot, Oscar
Bertipaglia, Alberto
Boekema, Hidde
Jain, Vishrut
Kegl, Marcell
Kotian, Varun
Lentsch, Ted
Lin, Yancong
Messiou, Chrysovalanto
Schippers, Emma
Tajdari, Farzam
Wang, Shiming
Xia, Zimin
Zaffar, Mubariz
Ensing, Ronald
Garzon, Mario
Alonso-Mora, Javier
Caesar, Holger
Ferranti, Laura
Happee, Riender
Kooij, Julian F. P.
Papaioannou, Georgios
Shyrokau, Barys
Gavrila, Dariu M.
author_facet de Groot, Oscar
Bertipaglia, Alberto
Boekema, Hidde
Jain, Vishrut
Kegl, Marcell
Kotian, Varun
Lentsch, Ted
Lin, Yancong
Messiou, Chrysovalanto
Schippers, Emma
Tajdari, Farzam
Wang, Shiming
Xia, Zimin
Zaffar, Mubariz
Ensing, Ronald
Garzon, Mario
Alonso-Mora, Javier
Caesar, Holger
Ferranti, Laura
Happee, Riender
Kooij, Julian F. P.
Papaioannou, Georgios
Shyrokau, Barys
Gavrila, Dariu M.
contents We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for environment perception, localization and mapping, motion planning, and control, integrated into a prototype vehicle. A key innovation is a motion planner based on Topology-driven Model Predictive Control (T-MPC). The guidance layer generates multiple trajectories in parallel, each representing a distinct strategy for obstacle avoidance or non-passing. The underlying trajectory optimization constrains the joint probability of collision with VRUs under generic uncertainties. To address extraordinary situations ("edge cases") that go beyond the autonomous capabilities - such as construction zones or encounters with emergency responders - the system includes an option for remote human operation, supported by visual and haptic guidance. In simulation, our motion planner outperforms three baseline approaches in terms of safety and efficiency. We also demonstrate the full system in prototype vehicle tests on a closed track, both in autonomous and remotely operated modes.
format Preprint
id arxiv_https___arxiv_org_abs_2505_04982
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation
de Groot, Oscar
Bertipaglia, Alberto
Boekema, Hidde
Jain, Vishrut
Kegl, Marcell
Kotian, Varun
Lentsch, Ted
Lin, Yancong
Messiou, Chrysovalanto
Schippers, Emma
Tajdari, Farzam
Wang, Shiming
Xia, Zimin
Zaffar, Mubariz
Ensing, Ronald
Garzon, Mario
Alonso-Mora, Javier
Caesar, Holger
Ferranti, Laura
Happee, Riender
Kooij, Julian F. P.
Papaioannou, Georgios
Shyrokau, Barys
Gavrila, Dariu M.
Robotics
Systems and Control
We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for environment perception, localization and mapping, motion planning, and control, integrated into a prototype vehicle. A key innovation is a motion planner based on Topology-driven Model Predictive Control (T-MPC). The guidance layer generates multiple trajectories in parallel, each representing a distinct strategy for obstacle avoidance or non-passing. The underlying trajectory optimization constrains the joint probability of collision with VRUs under generic uncertainties. To address extraordinary situations ("edge cases") that go beyond the autonomous capabilities - such as construction zones or encounters with emergency responders - the system includes an option for remote human operation, supported by visual and haptic guidance. In simulation, our motion planner outperforms three baseline approaches in terms of safety and efficiency. We also demonstrate the full system in prototype vehicle tests on a closed track, both in autonomous and remotely operated modes.
title A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation
topic Robotics
Systems and Control
url https://arxiv.org/abs/2505.04982