1,983 research outputs found
A Game-theory Analysis of Charging Stations Selection by EV Drivers
We address the problem of Electric Vehicle (EV) drivers' assistance
through Intelligent Transportation System (ITS). Drivers of EVs that are low in battery may ask a navigation
service for advice on which charging station to use and which route
to take. A rational driver will follow the received advice, provided
there is no better choice
i.e., in
game-theory terms, if such advice corresponds to a Nash-equilibrium
strategy.
Thus, we model the problem as a game: first we propose a
congestion game, then a game with congestion-averse utilities,
both admitting at least
one pure-strategy Nash equilibrium. The
former represents a practical scenario with a high level of realism,
although at a high computational price. The latter neglects some
features of the real-world scenario but it exhibits very low
complexity, and is shown to provide results that, on average,
differ by 16% from those obtained with the former approach.
Furthermore, when drivers value the trip time most, the average
per-EV performance yielded by the Nash
equilibria and the one attained by solving a
centralized optimization problem that minimizes the EV trip time
differ by 15% at most.
This is an important result, as minimizing this quantity implies reduced road traffic congestion
and energy consumption, as well as higher user
satisfaction
Vehicular Networks with Infrastructure: Modeling, Simulation and Testbed
This thesis focuses on Vehicular Networks with Infrastructure. In the examined scenarios, vehicular nodes (e.g., cars, buses) can communicate with infrastructure roadside units (RSUs) providing continuous or intermittent coverage of an urban road topology. Different aspects related to the design of new applications for Vehicular Networks are investigated through modeling, simulation and testing on real field. In particular, the thesis: i) provides a feasible multi-hop routing solution for maintaining connectivity among RSUs, forming the wireless mesh infrastructure, and moving vehicles; ii) explains how to combine the UHF and the traditional 5-GHz bands to design and implement a new high-capacity high-efficiency Content Downloading using disjoint control and service channels; iii) studies new RSUs deployment strategies for Content Dissemination and Downloading in urban and suburban scenarios with different vehicles mobility models and traffic densities; iv) defines an optimization problem to minimize the average travel delay perceived by the drivers, spreading different traffic flows over the surface roads in a urban scenario; v) exploits the concept of Nash equilibrium in the game-theory approach to efficiently guide electric vehicles drivers' towards the charging stations. Moreover, the thesis emphasizes the importance of using realistic mobility models, as well as reasonable signal propagation models for vehicular networks. Simplistic assumptions drive to trivial mathematical analysis and shorter simulations, but they frequently produce misleading results. Thus, testing the proposed solutions in the real field and collecting measurements is a good way to double-check the correctness of our studie
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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
QoE-aware power management in vehicle-to-grid networks:a matching-theoretic approach
Frequency, time and places of charging and discharging have critical impact on the Quality of Experience (QoE) of using Electric Vehicles (EVs). EV charging and discharging scheduling schemes should consider both the QoE of using EV and the load capacity of the power grid. In this paper, we design a traveling plan-aware scheduling scheme for EV charging in driving pattern and a cooperative EV charging and discharging scheme in parking pattern to improve the QoE of using EV and enhance the reliability of the power grid. For traveling planaware scheduling, the assignment of EVs to Charging Stations (CSs) is modeled as a many-to-one matching game and the Stable Matching Algorithm (SMA) is proposed. For cooperative EV charging and discharging in parking pattern, the electricity exchange between charging EVs and discharging EVs in the same parking lot is formulated as a many-to-many matching model with ties, and we develop the Pareto Optimal Matching Algorithm (POMA). Simulation results indicates that the SMA can significantly improve the average system utility for EV charging in driving pattern, and the POMA can increase the amount of electricity offloaded from the grid which is helpful to enhance the reliability of the power grid
A cognitive process approach to modeling gap acceptance in overtaking
Driving automation holds significant potential for enhancing traffic safety.
However, effectively handling interactions with human drivers in mixed traffic
remains a challenging task. Several models exist that attempt to capture human
behavior in traffic interactions, often focusing on gap acceptance. However, it
is not clear how models of an individual driver's gap acceptance can be
translated to dynamic human-AV interactions in the context of high-speed
scenarios like overtaking. In this study, we address this issue by employing a
cognitive process approach to describe the dynamic interactions by the oncoming
vehicle during overtaking maneuvers. Our findings reveal that by incorporating
an initial decision-making bias dependent on the initial velocity into existing
drift-diffusion models, we can accurately describe the qualitative patterns of
overtaking gap acceptance observed previously. Our results demonstrate the
potential of the cognitive process approach in modeling human overtaking
behavior when the oncoming vehicle is an AV. To this end, this study
contributes to the development of effective strategies for ensuring safe and
efficient overtaking interactions between human drivers and AVs
Simulation Research on Driving Behaviour of Autonomous Vehicles on Expressway Ramp Under the Background of Vehicle-Road Coordination
Constructing a risk model with the subject of autonomous vehicles to screen out the vehicles of potential conflicts and analyze their choices under different strategies. Based on the co-simulation of Python and SUMO, establishing a model of on-ramp merge driving behaviour of autonomous vehicles based on non-cooperative static game. Under this model, the experiment results that the average speed in the merging area is increased by 12.7%, the standard deviation of the average speed is reduced by 35.46%, and the number of the vehicles successfully merged before the end of the merging area is 4.86 times that of traditional method, indicate that the model can effectively help the vehicles be merged and improve the traffic efficiency to a certain extent
Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey
Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development
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