Environment Aware Localization with BLE Fingerprinting for the Next Generation PEPS system

Abstract

Traditional Passive Entry and Passive Start (PEPS) systems use a physical key fob, LF transmitters, and UHF communication to control a vehicle's door and engine. The car's Electronic Control Unit (ECU) can unlock the door or trunk, and it can also start the engine depending on the key fob's positions and the command received from the key. In this paper, we use smartphone with Bluetooth Low Energy (BLE) beacons to implement the next-generation PEPS system, replacing the LF/UHF technology and the physical key fob used in the existing PEPS system. With BLE fingerprinting, we can estimate the position of a smartphone by comparing BLE signal strengths of the current location with pre-recorded signal strengths of reference points. However, unlike the existing indoor localization, BLE-based PEPS systems need to consider the impact of changing environment due to the mobility of a vehicle since BLE signal strength is highly affected by surrounding environment such as neighboring cars and walls. If pre-recorded environment and the actual environment are different, localization accuracy can be greatly reduced. To address this issue we propose a new BLE-based localization scheme that can estimate the surrounding environment of the vehicle through beacons and scanners attached to the vehicle. We have evaluated our proposed BLE-based PEPS system by using Hyundai LF Sonata as a test vehicle. For comparative evaluation, we also test BLE ranging model, which determines the smartphone position depending on the strength and the type of detected beacons. In an open space testbed with no neighboring vehicles, the ranging model produces 90.1% positioning accuracy on average. In contrast, the proposed BLE-fingerprinting model achieves 94.6% accuracy on average without dark areas. In addition, in the same testbed with neighboring vehicles, the proposed BLE-fingerprinting PEPS system achieves 90.5% accuracy on average with environment adaptation while only 62% accuracy without environmental knowledge.