A Novel Crash Detection Algorithm for Two-Wheeled Vehicles

Autor:	Silvia Strada, Claudio De Tommasi, Simone Gelmini, Sergio M. Savaresi, Mara Tanelli
Rok vydání:	2021
Předmět:	Control and Optimization Event (computing) Computer science business.industry Real-time computing Crash Bicycle and motorcycle dynamics Vehicle dynamics Artificial Intelligence GNSS applications Automotive Engineering Global Positioning System Telematics business Access time
Zdroj:	IEEE Transactions on Intelligent Vehicles. 6:88-99
ISSN:	2379-8904 2379-8858
DOI:	10.1109/tiv.2020.3028462
Popis:	This paper presents a crash detection strategy for motorcycles, using GPS/GNSS and inertial measurements collected by telematic e-Boxes. The primary goal is to jointly minimize the access time of the emergency services and to accurately store the event dynamics for further investigation on accident's responsibilities. For motorcycles, unlike for cars, crash events cannot always be detected by monitoring abnormal longitudinal decelerations solely. Thus, this work proposes a novel method that detects and classifies the severity of crash-like situations. The proposed approach follows a new paradigm that improves the detection performance, better generalizing the motorcycle dynamics with respect to the specific vehicle and the driving style. The proposed approach has been tested and validated with experimental data, covering both motorsport and naturalistic scenarions, involving several riders, different road conditions and vehicles.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96a7c1969406ee8e538d5c1e34179006 https://doi.org/10.1109/tiv.2020.3028462 Zobrazit plný text záznamu