Szczegóły publikacji
Opis bibliograficzny
Active safety for individual and connected vehicles using mobile phone only / Mateusz PACIOREK, Piotr WAWRYKA, Adrian KŁUSEK, Przemysław Kalawski, Michał Kosowski, Andrzej Piechowicz, Julia Plewa, Michał Powroźnik, Michał Śledź, Aleksander BYRSKI, Marcin KURDZIEL, Wojciech TUREK // W: MoMM 2019 [Dokument elektroniczny] : the 17th international conference on Advances in Mobile computing & Multimedia : December 2–4, 2019, Munich, Germany / eds. Pari Delir Haghighi [et al.]. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, 2019. — e-ISBN: 978-1-4503-7178-0. — S. 36–45. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dl.acm.org/doi/pdf/10.1145/3365921.3365924 [2020-04-06]. — Bibliogr. s. 45, Abstr.
Autorzy (12)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129067 |
|---|---|
| Data dodania do BaDAP | 2020-06-17 |
| DOI | 10.1145/3365921.3365924 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | 17th international conference on Advances in Mobile Computing & Multimedia |
Abstract
Recently there has been an increasing interest in telematics solutions for vehicles. These systems provide real-time danger detection, driving style evaluation or crash detection services. The provided information can significantly increase driving safety, help to improve driving style, support rescue operations and enable better-informed insurance pricing. While recently introduced vehicles often provide inbuilt telematics systems, older cars usually lack such functionalities. One option to remedy this issue is to develop telematics solutions that are based on sensors other than the original car equipment. In this paper we present our telematics solution that employs smartphone sensors. This research stems from our previous work on crash detection which we substantially expanded towards a system for automatic profiling of driving style, detection of driving anomalies, and community-based identification of dangerous places on public roads. We describe our approach to the processing of sensor data, driving profile construction, anomaly detection, and interaction with the driver, illustrating these aspects with selected tangible results.