Research on Traffic Adaptability Testing and Assessment Method of Connected Vehicle Under Platoon Driving Scenario

Abstract

Applications of connected vehicles (CVs) will be widely deployed in the near future owing to the rapid development of vehicle-to-everything (V2X) communication technology. However, when CVs are running in a real traffic environment, they may encounter some problems (i.e., traffic adaptability problems) that are not noticed in simulations, hardware/software-in-the-loop tests, and closed area tests. This leads to an unexpected poor performance of CV technology in a real traffic environment. Therefore, there is a need to test and assess CV technology in a real traffic environment before the deployment of CV applications. This study concentrates on a platoon driving scenario to explore the testing and assessment methods for the traffic adaptability of CV applications. The concept of traffic adaptability, which includes the aspects of efficiency, safety, and comfortableness, is defined, and an indicator system of traffic adaptability is established as the approval standard of CV traffic adaptability. A dedicated and decentralized field test system that can provide a simple CV environment was developed and used for field tests in real urban traffic environments in Beijing. Based on the data acquired from the field test, a traffic adaptability assessment of CV under platoon driving scenario utilizing the relevant indicator system is executed. The results show that with the assistance of CV technology provided by the developed field test system, that is, CV-DAVS, the traffic adaptability values of the CV platoon on the efficiency, safety, and comfort aspects are 109.98%, 113.07%, and 103.85%, respectively. Therefore, the traffic adaptability of CV under platoon driving scenario can be approved, and its overall traffic adaptability is 108.97%.INDEX TERMS connected vehicle, traffic adaptability, platoon driving, field test, testing system development, indicator analysis

Research on the Physics–Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles

“…The ISO and ASAM deconstruct scenarios into several levels with the representation of OpenX [ 19 , 20 ]. The element level specifies the types and forms of the scenario elements in detail, the structure level is comprised of the element classification and scenario models, and the semantic level interprets the perceptive mode and diving capability between the ego vehicle and background vehicles [ 21 ]. In contrast, data learning and logic classification methods, such as classification trees, data clustering, or expert experience database, are developed in scenario element extraction [ 22 , 23 ].…”