Autonomous Ground Vehicles—Concepts and a Path to the Future

By Thorsten Luettel, Michael Himmelsbach, Hans-Joachim Wünsche. Proceedings of the IEEE, 2012

337 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7cd2d749d5c9cce1c79b7fe9bd71d51f1b5fde66

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Autonomous Vehicle Safety: An Interdisciplinary Challenge (2017, background): “…approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly unstructured environments, to appropriate regulatory…”
A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving (2017, background): “Although preliminary successes have been achieved with AVs, there are still many intractable issues from both a technical and a societal perspective [2,6].”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “Thus, vehicles are not aware of potential threats from the blind areas of their image-based sensors and distance-sensing devices [47].”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “Meanwhile, we are witnessing the growing intelligence of vehicles, due to recent advancements in embedded systems, navigation, sensors, data acquisition and dissemination, and big data analytics [11], [12].”
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners (2018, background): “Similarly, for automated vehicles, a decision-making system must select a route through the road network from its current position to the requested destination [21,22,23].”
A Survey of Deep Learning Applications to Autonomous Vehicle Control (2019, background): “autonomous vehicles in terms of better fuel economy [9], [10], reduced pollution, car sharing [11], increased productivity, and”
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond (2021, background): “and traffic flow, less fuel consumption and pollution, and hopefully fewer accidents [76].”