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Meng Wang

Researcher at Delft University of Technology

Publications -  105
Citations -  3786

Meng Wang is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Traffic flow & Platoon. The author has an hindex of 27, co-authored 102 publications receiving 2228 citations. Previous affiliations of Meng Wang include Ministry of Communications & Zhejiang University.

Papers
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Game theoretic approach for predictive lane-changing and car-following control

TL;DR: In this paper, a receding horizon control approach for automated driving systems is proposed, where tactical-level lane change decisions and control-level accelerations are jointly evaluated under a central mathematical framework.
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Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment

TL;DR: In this article, an eco-driving system for an isolated signalized intersection under partially connected and automated vehicles (CAV) environment is proposed, which prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow.
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Rolling horizon control framework for driver assistance systems. Part II: Cooperative sensing and cooperative control

TL;DR: In this paper, the authors further extend the control framework for driver assistance systems in Part I to cooperative systems, where equipped vehicles can exchange relevant information via vehicle-to-vehicle communication to improve the awareness of the ambient situation and to manoeuvre together under a common goal.
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Rolling horizon control framework for driver assistance systems. Part I: Mathematical formulation and non-cooperative systems

TL;DR: In this paper, a rolling horizon control framework for driver assistance systems is proposed, where accelerations of equipped vehicles are controlled to optimise a cost function reflecting different control objectives, taking into account the predicted behaviour of other vehicles.
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Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability

TL;DR: In this article, a serial distributed model predictive control (MPC) approach for connected automated vehicles (CAVS) is developed with local stability (disturbance dissipation over time) and multi-criteria string stability.