Towards the Trilateration of Non-Connectable Vehicles for Digital Twin: Estimating Distance Between Vehicles Based on Camera-Lidar Data Fusion

With emerging technologies in connected cooperative automated mobility, on-road situation awareness of vehicles has been broadly discussed recently. To achieve greater situation awareness cloud-based digital twin could be utilized. The data from this model could subsequently be used in collision avoidance systems. Unfortunately, transitioning from vehicles with low-level automation and no connectivity to highly automated and connected vehicles will take time. During the period, vehicles with connectivity, together with non-connectable vehicles, will share the same roads. To enhance mobility safety, methods for announcing the dynamics and positions of non-connectable vehicles must be implemented. In this paper, the authors proposed a simplistic method to determine the position of the non-connectable vehicle based on trilateration in two-dimensional space. Also, the paper provides proof-of-concept of estimating the distance between a connected and a non-connectable vehicle, which is the first step toward a proposed solution.

I. Introduction

In the past few years, connected collaborative and automated mobility, together with the digital twin of roads and vehicles, seem to be key approaches to enhancing road safety [1]. The key benefit of those approaches is that the digital twin can acquire data from multiple sources like vehicles, infrastructure, weather data, and traffic data. Afterward, the twin could process these data and use them to support intelligent vehicle decisions with more information that the vehicle could obtain by itself.