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

Han Jun Bae, Lynn Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538676462
DOIs
Publication statusPublished - 2019 Apr
Event2019 IEEE Wireless Communications and Networking Conference, WCNC 2019 - Marrakesh, Morocco
Duration: 2019 Apr 152019 Apr 19

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2019-April
ISSN (Print)1525-3511

Conference

Conference2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
Country/TerritoryMorocco
CityMarrakesh
Period19/4/1519/4/19

Bibliographical note

Funding Information:
V. CONCLUSION In this paper, we replace the LF-based positioning scheme with physical key fob in the existing PEPS system with BLE-based keyless positioning scheme with smartphone. We use 6 beacons that are attached at the inside and outside of a vehicle and locate the position of the smartphone by using BLE beacon signals. We use two BLE localization models: BLE ranging model and BLE fingerprinting model for the PEPS localization function. Based on our experimentation results on a commercial vehicle, our BLE fingerprinting model with 6 beacons can achieve 94.6% accuracy for 5 areas (inside, left, right, front and back) positioning for an open space environment. The proposed BLE fingerprinting model generally achieves higher localization performance compared to the existing BLE ranging model for all the test cases we evaluated. However, this pure BLE fingerprinting model may not adapt to the environmental change caused by the vehicle movement. To address this issue, we propose an environment prediction algorithm that can dynamically predict the environment surrounding the vehicle by using additional scanners attached outside of the vehicle. To perform the prediction, we construct additional radio maps called environment radio maps for various target environments. With 4 scanners and 6 beacons, the proposed PEPS system with environment prediction achieves 90.5% accuracy on average while the pure BLE fingerprinting model can achieve only 62% accuracy without environmental knowledge. With this new BLE-based positioning scheme, we can effectively remove physical key fob as well as LF antennas by using BLE beacons and scanners for both smartphone positioning and remote control communication in the next generation PEPS system. ACKNOWLEDGEMENT This work was supported by the National Research Found ation of Korea(NRF) grant funded by the Korea government(M SIP) (NRF-2017R1A2B2009641) and by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2018-2015-0-00363) supervised by the IITP (Institute for Information & communications Technology Promotion). REFERENCES [1] Hisashi Oguma, Naoki Nobata, et al., “Passive keyless entry system for long term operation” Proc. IEEE WoWMoM 2011. [2] Lepek, Paul, and Paul Hartanto. "RF design considerations for passive entry systems." Atmel Automotive Compilation Vol. 6, No. 20, 2009. [3] Kyung-Hyun Koo, and Lynn Choi. “BLE-based localization of smartphone for the passive entry and passive start system” ICNGC 2017b, 2017. [4] Liu, H., Yang, J., Sidhom, S., Wang, Y., Chen, Y., and Ye, F., “Accurate Wi-Fi based localization for smartphones using peer assistance,” in IEEE Transactions on Mobile Computing, 2014, pp. 2199-2214 [5] Bahl, Paramvir, and Venkata N. Padmanabhan. "RADAR: An in-building RF-based user location and tracking system." Proc. IEEE INFOCOM 2000. [6] Karani, Rushab, et al. "Implementation and design issues for using Bluetooth low energy in passive keyless entry systems." IEEE INDICON, 2016.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • BLE
  • Beacon
  • Fingerprinting
  • Key Fob
  • Localization
  • PEPS
  • Smart Car

ASJC Scopus subject areas

  • General Engineering

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