Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods
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REVIEW ARTICLE
Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods Jeferson Menegazzo1 · Aldo von Wangenheim1 Received: 31 January 2020 / Accepted: 27 July 2020 © Springer Nature Singapore Pte Ltd 2020
Abstract In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle’s movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state; and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications. Keywords Road surface anomaly · Road conditions · Driver behaviour · Driving style · Inertial sensors · Intelligent transport systems
Introduction Vehicular perception comprises the identification of diverse information related to the context where the vehicle is inserted. This information, recognized as patterns, can be classified between exteroception and proprioception, as depicted in Fig. 1. The exteroception aims to understand the environment outside the vehicle, performing the recognition of characteristics of the path in which the vehicle moves. These characteristics include transient events such as anomalies and obstacles; and persistent events, such as composition type and track surface quality. Proprioception, the vehicle’s self perception, aims to understand the vehicle movements to identify its own behavior. These identifications can also
* Jeferson Menegazzo [email protected] Aldo von Wangenheim [email protected] 1
Graduate Program in Computer Science (PPGCC), Image Processing and Computer Graphics Lab (LAPIX), Department of Informatics and Statistics (INE), Brazilian Institute for Digital Convergence (INCoD), Federal University of Santa Catarina (UFSC), Florianópolis, Santa Catarina, Brazil
be transient, such as acceleration, braking, turning right or left, skidding and aquaplaning; and persistent, such as driving behavior profiles. In recent years, vehicle perception information has been applied to a variety of domains. Sensory information about the road surface, such as the pavement conservation state and geolocation of anomalies, has been used mainly in the transport infrastructure monitoring, helping maintenance. Other applications involve advanced driver assistance systems (ADAS) and evaluation software for control and quality assurance in civil engineering [52]. Information on proprioception, such as driving p
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