Comparison of Distance Prediction with Bluetooth Low Energy and GNSS for Vulnerable Road Users Safety

A key enabler of future Intelligent Transportation Systems (ITS) is the ability to use wireless communication technologies to detect the presence of other road traffic participants. This is particularly true for pedestrians and cyclists, who are often not detected by current systems. Bluetooth Smart (BLE), is a low power, low cost, low bandwidth wireless technology that is being used in a variety of applications and is available in many embedded systems and mobile devices. Its is designed to enable the periodic transmission of beacons to detect the presence of other devices in the vicinity and many applications such as COVID contact tracing or asset tracking use this feature. Based on BLE-enabled distance estimations between e.g., cyclists and vehicles, collision avoidance systems could warn or intervene.

Goals of the thesis

This thesis will compare the BLE-enabled distance estimations with measurements of Global Navigation Satellite Systems (GNSS) in order to evaluate its performance. Using embedded computers equipped with Bluetooth controllers, one for sending and one for receiving BLE beacons, RSSI measurements, based on which the distance of the embedded computers is estimated, are conducted in an open filed and in an urban environment. Additionally, GNSS measurements, using u-blox evaluation kits, are carried out in the same manner and based on the position data the distance is calculated. Based on the results the quality of the BLE-enabled distance estimations can be assessed using the GNSS measurements as a baseline. A detailed written discussion of fundamentals, related work, experiment design, and obtained results concludes the thesis.

Collaboration

This thesis is part off a larger collaboration with teams at TU Braunschweig and TU Ilmenau.

Keywords

Linux, Hardware, Practical

Literature

[1] D. Giovanelli and E. Farella, “RSSI or Time-of-flight for Bluetooth Low Energy Based Localization? An Experimental Evaluation,” 11th IFIP Wireless and Mobile Networking Conference (WMNC), Prague, Czech Republic, 2018, doi: 10.23919/WMNC.2018.8480847.

[2] M. Wu, B. Ma, Z. Liu, L. Xiu and L. Zhang, “BLE-horn: A Smartphone-based Bluetooth Low Energy Vehicle-to-Pedestrian Safety System,” 9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, 2017, doi: 10.1109/WCSP.2017.8171180.

[3] https://github.com/IanHarvey/bluepy

[4] https://github.com/mh-/exposure-notification-ble-python/blob/master/README.md

[5] https://learn.adafruit.com/pibeacon-ibeacon-with-a-raspberry-pi

[6] https://learn.adafruit.com/introduction-to-bluetooth-low-energy/gatt

[7] https://developer.android.com/guide/topics/connectivity/bluetooth/connect-gatt-server

[8] https://github.com/mengguang/pi-ble-uart-server

[9] https://github.com/labapart/gattlib

Contact

• M. Franke
• C. Sommer