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

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

Conference Paper (2025)

Author(s)

O.M. de Groot (TU Delft - Intelligent Vehicles)

A. Bertipaglia (TU Delft - Intelligent Vehicles)

H.J. Boekema (TU Delft - Intelligent Vehicles)

V. Jain (TU Delft - Intelligent Vehicles)

M. Kegl (TU Delft - Intelligent Vehicles)

V. Kotian (TU Delft - Intelligent Vehicles)

T. Lentsch (TU Delft - Intelligent Vehicles)

Y. Lin (Student TU Delft)

C. Messiou (TU Delft - Intelligent Vehicles)

E. Schippers (Student TU Delft)

F. Tajdari (TU Delft - Intelligent Vehicles)

S. Wang (Student TU Delft)

Z. Xia (TU Delft - Intelligent Vehicles)

M. Zaffar (TU Delft - Intelligent Vehicles)

R.M. Ensing (TU Delft - Intelligent Vehicles)

M.A. Garzon Oviedo (TU Delft - Intelligent Vehicles)

J. Alonso-Mora (TU Delft - Learning & Autonomous Control)

Holger Caesar (TU Delft - Intelligent Vehicles)

L. Ferranti (TU Delft - Learning & Autonomous Control)

R. Happee (TU Delft - Intelligent Vehicles)

J.F.P. Kooij (TU Delft - Intelligent Vehicles)

G. Papaioannou (TU Delft - Intelligent Vehicles)

B. Shyrokau (TU Delft - Intelligent Vehicles)

D. Gavrila (TU Delft - Intelligent Vehicles)

Research Group

Intelligent Vehicles

DOI related publication

https://doi.org/10.1109/IV64158.2025.11097542

To reference this document use:

https://resolver.tudelft.nl/uuid:a1eda7e0-76cd-48a0-a773-5ed21c97a3cd

More Info

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Publication Year

2025

Language

English

Research Group

Intelligent Vehicles

Bibliographical Note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals 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.@en

Pages (from-to)

2482-2489

ISBN (electronic)

979-8-3315-3803-3

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

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.

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