Autonomous vehicle positioning with GPS in urban canyon environments
By Youjing Cui, Shuzhi Sam Ge. Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001
385 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/e9aaf0acc568a45e91f75c91a9bdff58ce5453cf
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Alexandre M. Bayen
- Dieter Fox
- Washington Yotto Ochieng
- Robert B. Noland
- Mohammed A. Quddus
- Liefeng Bo
- Xiaofeng Ren
- Sebastian Thrun
In Artificial Intelligence
- Dieter Fox
- Eugene Vinitsky
- Donald J. Patterson
- Henry A. Kautz
- Alexandre M. Bayen
- Washington Yotto Ochieng
- Lin Liao
- Cathy Wu
Citations in the corpus
- Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control (2017, background): “Robotics has already demonstrated tremendous potential in improving transportation systems through autonomous vehicles research; highly related problems include localization [12], [13], [14], path planning [15], [16], collision avoidance [17], and perception [18] problems.”
- Cyber Threats Facing Autonomous and Connected Vehicles: Future Challenges (2017, background): “These limitations are often overcome with the use of an increased number of satellites [16] to provide better coverage.”
- Current map-matching algorithms for transport applications: State-of-the art and future research directions (2007, methodology): “, 2002), an interacting multiple model (Cui and Ge, 2003), a fuzzy logic model (e.”
- State of the Art of Automated Buses (2018, neutral): “Moreover, GNSSs need the direct connection between a receiver on the Earth and more satellites at the same time resulting in loss of accuracy in the case of hidden satellites, e.g., the so-called urban canyons, i.e., city roads surrounded by tall buildings [35].”
- Inferring High-Level Behavior from Low-Level Sensors (2003, background): “One strand of such work concerns tracking and predicting a person’s movements in outdoor settings using GPS [1,2,3,4].”
- Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, background): “Robotics has continued to demonstrate tremendous potential in improving transportation systems through AV research; highly related problems include localization [28], [29], [30], path planning [31], [32], collision avoidance [33], and perception [34].”