Highway Toll Collection Method for Connected Automated Vehicle Platooning Using Spatio-Temporal Grid Reservation

In the intelligent transportation system, the autonomous vehicle platoon is a promising concept for addressing traffic congestion problems. However, under certain conditions, the platoon’s advantage cannot be properly developed, especially when stopping for electronic toll collection (ETC) to pay the toll fee using the highway. This study proposes a software architectural platform that enables connected automated vehicles to reserve a grid-based alternative approach to replace current highway toll collection systems. A planned travel route is reserved in advance by a connected automated vehicle in a platoon, and travel is based on reservation information. We use driving information acquired by communication mechanisms installed in connected automated vehicles to develop a dynamic map platform that collects highway toll tax based on reserving spatio-temporal grids. Spatio-temporal sections are developed by dividing space and time into equal grids and assigning a certain road tax rate. The results of the performance evaluation reveal that the proposed method appropriately reserves the specified grids and collects toll taxes accurately based on a spatio-temporal grid with minimal communication time and no data package loss. Likely, using the proposed method to mediate driving on a one-kilometer route takes an average of 36.5 seconds, as compared to ETC and the combination of ETC and freeway road lane methods, which take 46.6 and 53.8 seconds, respectively, for 1000 vehicles. Consequently, our proposed method’s travel time improvements will reduce congestion by more effectively exploiting road capacity as well as enhance the number of platoons while providing non-stoppable travel for autonomous vehicles.

Reachability-Based Packet Scheduler of Multipath QUIC for Heterogeneous Mobile Networks

In recent years, we need more bandwidth to enjoy entertainment contents such as video streaming, music and online gaming. To gain enough bandwidth, technologies that combine bandwidth by using multiple interfaces at same time are desired. Multipath transport protocols which combine multiple paths for packet delivery at the transport layer are a promising technology. Such protocols have a mechanism, called “packet scheduler”, to select the interface to send a packet. However, existing studies of the packet scheduler have not explicitly considered the compatibility of mobility with bonding of bandwidth. Therefore, when smartphone users move out of coverage of communication networks such as wireless Local Area Network (LAN) and Long Term Evolution (LTE) by vehicle, packet loss occurs, leading to a decrease of throughput. In this study, we propose a packet scheduler that selects an appropriate communication path so that packets can reach the peer before it moves out of the coverage. Based on routes of a vehicle and the position and communication range of the access point, the time at which a communication path will be lost is predicted. In addition, we employ MPQUIC (Multipath QUIC (Quick UDP Internet Connections)), which is a multipath transport protocol proposed as the extension of QUIC protocol, to reduce the ACK packet loss in multipath communication, and to reduce the time until the starts of retransmission. We evaluated the number of packet losses, the throughput and the time until starts of retransmission using a simulator and show the superiority of proposed method.

Evaluation of Effective Vehicle Probe Information Delivery with Multiple Communication Methods

Vehicle probe information delivery systems can be broadly divided into the center type and center-less type. Since conventional center-type information delivery systems generate a large load on the communications infrastructure and data center, research efforts have come to be focused on the centerless type. However, existing vehicle probe information delivery systems suffer from various problems including a limited service area, low delivery efficiency, and lack of immediacy in delivery. Our objective in this study is efficient delivery of vehicle probe information as needed. We propose a delivery scheme that uses vehicle-to-vehicle communication, infrastructure-to-vehicle communication, and mobile communication as well as Geo cast. This combined use of multiple communication methods achieves efficient information delivery by changing the communication method to fit the current situation. The results of an evaluation by simulation showed that the proposed scheme could deliver information efficiently in a variety of environments.

A Promising Distance-Based Gasoline Tax Charging System Based on Spatio-Temporal Grid Reservation in the Era of Zero-Emission Vehicles

Fuel taxes are still a primary funding source for the development and maintenance of transportation infrastructure. Such a tax is collected as a flat fee from the importer or producer of the taxable fuel product. Fuel-efficiency improvements and the adoption of zero-emission vehicles result in a continuous decrease in gasoline tax revenues. This paper proposes a novel distance-based alternative method to replace current gasoline tax collection systems in Japan by providing a software architecture platform. In this platform, we utilize driving information gathered via communication mechanisms installed in connected automated vehicles to develop a system that collects gasoline tax based on reserving spatio-temporal grids. Spatio-temporal sections are created by dividing space and time into equal grids and a designated tax charge is assigned. Connected automated vehicles reserve a planned travel route in advance and travel based on reservation information. The performance evaluation results indicate that the proposed system adequately reserves the requested grids and accurately collects gasoline taxes based on a spatio-temporal grid with minimum communication time and no data package loss. The proposed method is based on micro travel distance charges, which generates gasoline tax revenue by 5.7 percent for model year 2022 and 21.8 percent for model year 2030 as compared to the current flat-fee system.