Influence of Realistic Perception and Surroundings on Qualitative Results in Automated and Connected Vehicle Simulation

Automated and connected vehicles are emerging in the market. Currently, solutions are being proposed to use these technologies for cooperative driving, which can significantly improve road safety. Vehicular safety applications must be tested before deployment. It is challenging to verify and validate them in the real world. Therefore, simulation is used for this purpose. Modeling these technologies necessitates coupled the use of traffic flow, vehicle dynamics, communication network, perception and signal propagation models. State-of-the-art tools exist in these domains; however, they lack full domain coverage, and the low-level processes that affect each vehicle’s behavior are often left unaccounted for, which (according to complex system theory) can lead to erroneous assumptions about system-level outcomes. This paper analyzes the requirements for an integrated connected and automated vehicle simulation environment for simulating vehicle behavior with consideration of surrounding objects’ influence on machine perception and signal propagation. We discuss the shortcomings of existing methods, propose an architecture for a simulation environment with full domain coverage, and develop a preliminary version of the CAVISE integrated simulation environment for connected and automated vehicles using open-source tools. Scenarios of object detection and signal exchange were simulated, and data on the influence of surrounding objects on quantitative and qualitative changes in scenario simulation results was obtained. It was found that a more accurate description of surrounding objects and their influence on machine perception and signal transmission does lead to quantitative and qualitative changes in the measured parameters. This conclusion should be further used in simulation environment development for connected and automated vehicle technology verification and validation.