V
Vicente Milanés
Researcher at Renault
Publications - 132
Citations - 6151
Vicente Milanés is an academic researcher from Renault. The author has contributed to research in topics: Control theory & Intelligent transportation system. The author has an hindex of 31, co-authored 128 publications receiving 4626 citations. Previous affiliations of Vicente Milanés include Technical University of Madrid & University of California, Berkeley.
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Journal ArticleDOI
A Review of Motion Planning Techniques for Automated Vehicles
TL;DR: A review of motion planning techniques implemented in the intelligent vehicles literature, with a description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is presented.
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Cooperative Adaptive Cruise Control in Real Traffic Situations
Vicente Milanés,Steven E Shladover,John Spring,Christopher Nowakowski,Hiroshi Kawazoe,Masahide Nakamura +5 more
TL;DR: The design, development, implementation, and testing of a CACC system, which consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon, is presented.
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Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data
TL;DR: The Intelligent Driver Model (IDM) has been used for car-following modeling in this article to evaluate the performance of Adaptive Cruise Control (ACC) and Cooperative ACC (CACC) control systems.
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Automated On-Ramp Merging System for Congested Traffic Situations
TL;DR: An automated merging system that was developed with two principal goals, i.e., to permit the merging vehicle to sufficiently fluidly enter the major road to avoid congestion on the minor road and to modify the speed of the vehicles already on the main road to minimize the effect on that already congested main road, is described.
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An Intelligent V2I-Based Traffic Management System
TL;DR: A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed and showed good performance in testing in real-world scenarios.