Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system. Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services. Although it is generally infeasible for EVs to dictate their routes, we can design AV travel plans to fulfill certain system-wide objectives. In this paper, we focus on the AVs looking for parking and study how they can be led to appropriate parking facilities to support V2G services. We formulate the coordinated parking problem (CPP), which can be solved by a standard integer linear program solver but requires long computational time. To make it more practical, we develop a distributed algorithm to address CPP based on dual decomposition. We carry out a series of simulations to evaluate the proposed solution methods. Our results show that the distributed algorithm can produce nearly optimal solutions with substantially less computational time. A coarser time scale can improve computational time but degrade the solution quality resulting in possible infeasible solution. Even with communication loss, the distributed algorithm can still perform well and converge with only little degradation in speed.
There is some work studying intelligent parking in general. @cite_6 investigated availability of parking facilities for parking guidance and information systems. It developed a multivariate autoregressive model to account for the temporal and spatial relationship of parking availability. @cite_17 studied the uncoordinated parking space allocation for inexpensive limited on-street parking spots and expensive oversized parking lots. Some work focuses on AV parking. @cite_19 developed a control system for AV valet parking with a focus on steering control. @cite_39 designed an intelligent vehicle system to implement the AV valet parking service. However, they mainly targeted AV parking control in a confined parking area. Some investigate the parking issue for a larger area. @cite_34 proposed intelligent parking assistant architecture to manage parking spots to improve the quality of urban mobility. @cite_35 analyzed the impact of charging and discharging of EVs in parking lots on the power grid probabilistically. However, there is no thorough study on V2G based on AVs. In this work, we aim to bridge this research gap.
@cite_35 @cite_6 @cite_39 @cite_19 @cite_34 @cite_17
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Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss. This paper systematically explores the efficiency of uncoordinated parking space allocation in urban environments with two types of parking facilities. Drivers decide whether to go for inexpensive but limited on-street parking spots or for expensive yet overdimensioned parking lots, incurring an additional cruising cost when they decide for on-street parking spots but fail to actually acquire one. Their decisions are made under perfect knowledge of the total parking supply and costs and different levels of information about the parking demand, i.e., complete probabilistic information and uncertainty. We take a game-theoretic approach and analyze the parking-space allocation process in each case as resource selection game instances. We derive their equilibria, compute the related price-of-anarchy (PoA) values, and study the impact of pricing on them. It is shown that, under typical pricing policies on the two types of parking facilities, drivers tend to overcompete for the on-street parking space, giving rise to redundant cruising cost. However, this inefficiency can be alleviated through systematic manipulation of the information that is announced to the drivers. In particular, counterintuitive less-is-more effects emerge regarding the way that information availability modulates the resulting efficiency of the process, which underpin general competitive service provision settings. The autonomous vehicle valet parking service (AVP) enables a vehicle to drive and park without any human interaction. The driverless vehicle has to follow the scheduled route and be parked in a parking lot automatically. Most fundamental technologies for autonomous vehicle valet parking service are controlling vehicle driving and parking. For autonomous driving, the path to near goal parking lot has to be generated and control commands to follow the path have to be generated continuously. For autonomous parking, the controlling vehicle steer value has to be found to park the vehicle at slot well. And also the mobile assistant system is needed for driver to select the parking lot and monitoring the state of the vehicle in remote. In this paper, we have designed and implemented of an intelligent vehicle system for autonomous vehicle valet parking service in low speed limited parking area. The autonomous vehicle valet parking service (AVP) enables a vehicle to drive and park without any human interaction. The driverless vehicle has to follow the scheduled route and be parked in a parking lot automatically. Most fundamental technologies for autonomous vehicle valet parking service are controlling vehicle driving and parking. For autonomous driving, the path to near goal parking lot has to be generated and control commands to follow the path have to be generated continuously. For autonomous parking, the controlling vehicle steer value has to be found to park the vehicle at a parking lot well. We have used two turning radius circles to find vehicle steering control value about perpendicular and parallel parking method basically. These two vehicle turning radius circles are minimum radius circles in initial and are updated in real time as vehicle stop location in parking mode. In this paper, we have developed the control system for autonomous vehicle valet parking service in parking area with low speed limited. Parking is becoming an expensive resource in almost any major city in the world, and its limited availability is a concurrent cause of urban traffic congestion, and air pollution. In old cities, the structure of the public parking space is rigidly organised and often in the form of on-street public parking spots. Unfortunately, these public parking spots cannot be reserved beforehand during the pre-trip phase, and that often lead to a detriment of the quality of urban mobility. Addressing the problem of managing public parking spots is therefore vital to obtain environmentally friendlier and healthier cities. Recent technological progresses in industrial automation, wireless network, sensor communication along with the widespread of high-range smart devices and new rules concerning financial transactions in mobile payment allow the definition of new intelligent frameworks that enable a convenient management of public parking in urban area, which could improve sustainable urban mobility. In such a scenario, the proposed intelligent parking assistant (IPA) architecture aims at overcoming current public parking management solutions. This study discusses the conceptual architecture of IPA and the first prototype-scale simulations of the system. Parking guidance and information (PGI) systems are becoming important parts of intelligent transportation systems due to the fact that cars and infrastructure are becoming more and more connected. One major challenge in developing efficient PGI systems is the uncertain nature of parking availability in parking facilities (both on-street and off-street). A reliable PGI system should have the capability of predicting the availability of parking at the arrival time with reliable accuracy. In this paper, we study the nature of the parking availability data in a big city and propose a multivariate autoregressive model that takes into account both temporal and spatial correlations of parking availability. The model is used to predict parking availability with high accuracy. The prediction errors are used to recommend the parking location with the highest probability of having at least one parking spot available at the estimated arrival time. The results are demonstrated using real-time parking data in the areas of San Francisco and Los Angeles.