TR2024-093
Distributed Road-Map Monitoring Using Onboard Sensors
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- "Distributed Road-Map Monitoring Using Onboard Sensors", American Control Conference (ACC), July 2024.BibTeX TR2024-093 PDF
- @inproceedings{Zhang2024jul,
- author = {Zhang, Yanyu and Greiff, Marcus and Ren, Wei and Berntorp, Karl}},
- title = {Distributed Road-Map Monitoring Using Onboard Sensors},
- booktitle = {American Control Conference (ACC)},
- year = 2024,
- month = jul,
- url = {https://www.merl.com/publications/TR2024-093}
- }
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- "Distributed Road-Map Monitoring Using Onboard Sensors", American Control Conference (ACC), July 2024.
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MERL Contact:
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Research Areas:
Abstract:
Road maps for vehicle control and navigation systems are typically generated by mapping systems that are highly accurate but updated infrequently. However, changes to the roads are made at a higher frequency. Stored road maps may therefore not capture the true road well. To resolve this, we consider online road-map estimation using the type of sensors found in production cars. The map estimation for a given vehicle is based on a global positioning system, camera, steering wheel, and wheel-speed sensors. As each vehicle covers a limited amount of road, we leverage crowdsourced map estimates from multiple vehicles to get a more complete representation of the road map. High-fidelity simulation results indicate a reduction of the estimation error of roughly 15% when using 5 agents compared to the best single agent. Furthermore, we show that the method is capable of updating map segments that have large errors, for example, as may occur during road maintenance.
Related News & Events
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NEWS MERL researchers present 9 papers at ACC 2024 Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: Karl Berntorp; Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
In addition, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.