A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation
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arXiv
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| Format: | Preprint |
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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 |