Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration

TL;DR

This paper introduces a vehicular fog computing architecture with a trajectory calibration collision warning algorithm that improves real-time safety alerts despite communication delays and packet loss in vehicular networks.

Contribution

It proposes a novel trajectory calibration algorithm within a fog computing framework, enhancing real-time collision warning accuracy under communication uncertainties.

Findings

TCCW achieves the highest precision and recall in simulations.

Stable distribution effectively models V2I communication delay.

Simulation results outperform conventional cloud and fog warning methods.

Abstract

Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to distributed fog nodes. With the system architecture, we further propose a trajectory calibration based collision warning (TCCW) algorithm along with tailored communication protocols. Specifically, an application-layer vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable distribution with real-world field testing data. Then, a packet loss detection mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories based on received vehicle status including GPS coordinates, velocity, acceleration, heading direction, as well as the estimation of communication delay and the detection of packet loss. For performance evaluation, we build the simulation model and implement conventional solutions including cloud-based warning and fog-based warning without calibration for comparison. Real-vehicle trajectories are extracted as the input, and the simulation results demonstrate that the effectiveness of TCCW in terms of the highest precision and recall in a wide range of scenarios.