A Study of Estimating Lane-level Traffic Conditions Using Smartphone Data

Abstract

In an effort to improve automated driving and advanced driver assistance systems (AD/ADAS) performance, the industry is gradually moving toward greater vehicle autonomy. In this context, particular emphasis is being placed on the use of in-vehicle sensors and cameras. While these systems are primarily intended to replace or support the driver with invehicle devices, the ability to actively perceive non-visible areas can significantly improve vehicle safety and optimize route decisions. Traditional navigation maps (SD-MAP) have served their purpose, but often lack the necessary dynamism and accuracy; Vehicle-to-Everything (V2X) technology, particularly in the area of Vehicle-to-Network (V2N), is a growing area of interest due to the proliferation of smart phones and offer promising alternatives that leverage the strength of cellular networks. This study will evaluate the utility of smartphones as a central element of vehicle sensing and investigate their potential to provide critical lane-level recognition data for next-generation AD/ADAS applications. Through a comprehensive system architecture, smartphone GNSS data is processed to estimate lane-specific traffic conditions. In addition, current limitations and potential enhancements with respect to the use of OpenStreetMap (OSM) data will be detailed. While the accuracy of lane identification based solely on data from smartphones remains a challenge, this paper explores possible countermeasures, highlighting the integration of various data sources to achieve a holistic vehicle control system.