Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation

Abstract

Abstract:In recent years, vehicular communications systems have evolved and allowed for the improvement of adaptive cruise control (ACC) systems to make them cooperative (cooperative adaptive cruise control, CACC). Conventional ACC systems use sensors on the ego-vehicle, such as radar or computer vision, to generate their behavioral decisions. However, by having vehicle-to-X (V2X) onboard communications, the need to incorporate perception in the vehicle is drastically reduced. Thus, in this paper a CACC solution is proposed that only uses communications to make its decisions with the help of previous road mapping. At the same time, a method to develop these maps is presented, combining the information of a computer vision system to correct the positions obtained from the navigation system. In addition, the cut-in and cut-out maneuvers for a CACC platoon are taken into account, showing the tests of these situations in real environments with instrumented vehicles. To show the potential of the system in a larger-scale implementation, simulations of the behavior are provided under dense traffic conditions where the positive impact on the reduction of traffic congestion and fuel consumption is appreciated.

A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications

“…Therefore, we consider the need to supplement cellular traffic information with cellular communications. The work in [6] provided long-distance transmissions using cellular communication systems, and an intelligent transportation system was realized by load-mapping and queuing theory, which can effectively cope with congestion problems. The work in [7], through analysis and simulation, showed that C-V2X-the technology developed within the third-generation Partnership Project (3GPP) and designed to operate in both vehicle-to-vehicle and vehicle-to-network modes-is the prominent technology that can achieve V2X requirements and pave the most efficient way to connected and automated driving.…”

“…(5) The value-function sum of all sequences is the sequence value of the selection sequence. (6) The sequence with the highest value is the optimal selection sequence, and the base station of each vehicle in the sequence selects the best base station selection for the next second. To clearly describe the above mechanism, we provide an example in Figure 4.…”

Reduction of Fuel Consumption and Pollution Emissions in Inland Water Transport by Application of Hybrid Powertrain

“…These engines work most efficiently under a load of 80-85% of nominal power, and it is beneficial to create a situation where such a load is required to generate an appropriate amount of energy for the electric part of the drive system. In world literature, various analyzes [7], and methods [8][9][10] can be found, concerning the reduction of pollution emitted to the atmosphere by the internal combustion engines.…”

Communications and Driver Monitoring Aids for Fostering SAE Level-4 Road Vehicles Automation

“…An example of these early works on their impact is a study on flow control and congestion management in Automated Highway Systems [31]. More recently, some real test analyses and simulations have shown that safety and energy efficiency can be significantly improved for the connected automated vehicle as well as for the neighbouring human-driven vehicles (e.g., [32][33][34]).…”