Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project

By Paul Timothy Furgale, Ulrich Schwesinger, Martin Rufli, W. Derendarz, Hugo Grimmett, Peter Mühlfellner, S. Wonneberger, J. Timpner, S. Rottmann, Bo Li and 24 more. 2013

136 citations recorded by the source corpus.

Subfields: Automotive Engineering, Building and Construction, Computer Vision and Pattern Recognition, Reinforcement Learning

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Citations in the corpus

The Cityscapes Dataset for Semantic Urban Scene Understanding (2016, background): “Consequently, significant research efforts have gone into new vision technologies for understanding complex traffic scenes and driving scenarios [4, 16–18, 58, 62].”
Duckietown: An open, inexpensive and flexible platform for autonomy education and research (2017, background): “Our goal was to provide a complete “textbook” architecture that is comparable in complexity with full-scale implementations (e.g. [2]), while still being understandable by beginners.”
Planning and Decision-Making for Autonomous Vehicles (2018, background): “Impressive progress has been achieved in the last decade, yet recent works on self-driving vehicles still present limitations in the complexity of the environment and/or the speed of movement (12, 13).”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, background): “The DARPA Urban Challenge [20], and more recently the V-Charge Project [21] catalyzed the launch of research efforts into autonomous driving on urban road for numerous organizations.”
The Cityscapes Dataset (2015, background): “Despite this complexity, the ability to correctly analyze the environment is critical for the development of autonomous systems such as self-driving cars [5, 6].”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, unknown): “Still in the V-Charge project, [260] completed this system with height maps built from stereovision.”