Sensing-Assisted Neighbor Discovery for Vehicular Ad Hoc Networks

Abstract

In this paper, we propose a sensing-assisted neighbor discovery algorithm that utilizes the sensing capability of radar to improve the efficiency of neighbor discovery for vehicular ad hoc networks (VANETs). To store and manage the sensing information of radar, we design the sensing neighbor list (SNL) by analogy with the communication neighbor list (CNL). For vehicle mobility, we build a vehicle-to-vehicle (V2V) state evolution model and use extended Kalman filtering (EKF) to predict, track, and update the kinematic parameters of nodes, which are stored in the SNL. Specifically, the conversion relationship between CNL and SNL is implemented by the designed SNL based neighbor discovery (SBND) algorithm. Numerical simulation results show that the performance of the proposed algorithm is significant in terms of vehicle tracking and communication overhead reduction.