Cooperative Adaptive Cruise Control in Real Traffic Situations

By Vicente Milanés Montero, S. Shladover, J. Spring, C. Nowakowski, Hiroshi Kawazoe, Masahide Nakamura. IEEE transactions on intelligent transportation systems (Print), 2014

1,087 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/62dd876e58cec83733a9432a4cde3537fc722b9e

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In Automotive Engineering

In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Connected and automated vehicle systems: Introduction and overview (2018, background): “As explained in Shladover (2014), for road travel in the US today this represents a mean time between fatal crashes of more than 3 million vehicle hours of driving and a mean time between injury crashes of about 65,000 vehicle hours of driving.”
Benchmarks for reinforcement learning in mixed-autonomy traffic (2018, background): “Traffic microsimulators have been broadly accepted in the transportation engineering community as an acceptable prelude to the empirical evaluation of several complex automated tasks such as cooperative adaptive cruise control (CACC) [24] and mixed-autonomy traffic flow control [6].”
A Review of Motion Planning Techniques for Automated Vehicles (2016, neutral): “ceding vehicle [1].”
Control of Connected and Automated Vehicles: State of the Art and Future Challenges (2018, background): “CACC can exploit this additional piece of information to guarantee higher safety and smaller inter-vehicular distances [130, 131, 132, 133].”
Energy saving potentials of connected and automated vehicles (2018, background): “Experimental results in [141] showed string stable operation of a CACC”