This research is to integrate communication, traffic and driver behaviours, vehicle dynamics, and environment conditions in a unison framework to increase safety, reliability, and performance of connected and automated vehicles within a mix traffic condition.
To achieve this goal, TUPA has been developing an architecture that leverages existing tools like VISSIM, Driving simulator, Virtual Reality and Python to analyze and simulate multiple aspects of traffic including vehicles and pedestrians. This tool will enable to develop and validate techniques to improve traffic congestion, safety, and network performance.
In designing scenarios for the integration of Vissim and Driving simulator, a robust algorithm has been successfully implemented in the platform. This algorithm adapts its behaviour (traffic flow in transport) autonomously in response to the variation of network conditions.
Having human in the loop setting, alternative route guidance information will be displayed in driving simulator so that driver’s decision if he or she is willing to change the route based on the information will be recorded. In this process, the subject vehicle in the simulator and surrounding traffic in VISSIM will interact each other. In addition, all recoded outcomes from the driving simulator will be input to traffic simulation for the large network evaluation. The developed traffic simulation will identify typical traffic indicators such as delay and extreme delay, queue and the number of stops based on the scenarios developed in the driving simulator.
This research keeps developing with the KAIST team since middle of 2019. Transportation experts are aware that it is urgent to take measures to cope with mixed traffic between autonomous vehicles and conventional vehicles, which will be indispensable in the future. It is expected that this new traffic flow not only directly and indirectly leads to traffic accidents but also has a serious side effect on traffic efficiency. Until a fully autonomous vehicle occupies the road, new traffic control techniques are needed. In this study, we propose a new traffic signal controller that supports autonomous driving by driving the driver safely through the virtual environment. This study is expected to be synergistic effect of performance evaluation for future traffic in conjunction with the Connected ITS project and the K-City autonomous vehicle test bed currently underway in Korea. It is expected that many universities and research institutes will benefit from this research since Korea has not developed a human participatory machine learning platform using virtual reality or is in the very early stage.