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.
@article{arxiv.2505.04982,
title = {A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation},
author = {Oscar de Groot and Alberto Bertipaglia and Hidde Boekema and Vishrut Jain and Marcell Kegl and Varun Kotian and Ted Lentsch and Yancong Lin and Chrysovalanto Messiou and Emma Schippers and Farzam Tajdari and Shiming Wang and Zimin Xia and Mubariz Zaffar and Ronald Ensing and Mario Garzon and Javier Alonso-Mora and Holger Caesar and Laura Ferranti and Riender Happee and Julian F. P. Kooij and Georgios Papaioannou and Barys Shyrokau and Dariu M. Gavrila},
journal= {arXiv preprint arXiv:2505.04982},
year = {2025}
}