Opportunistic calibration of smartphone orientation in a vehicle

Khaleghi, Bahador; El-ghazal, A.; Hilal, Allaa R.; Toonstra, Jason; Miners, William B.; Basir, Otman

doi:10.1109/wowmom.2015.7158210

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Publications Accelerometer [167]- [172] Accelerometer, GNSS [71], [121], [173]- [177] Accelerometer, GNSS, Magnetometer [53], [99], [178]- [180] Gyroscope [181] IMU [122], [140], [182] IMU, GNSS [13], [54], [183], [184] IMU, Magnetometer [185], [186] IMU, GNSS, Magnetometer [187], [188] discuss supplementary information sources and how these can be used to improve the navigation solution.…”

Section: Sensorsmentioning

confidence: 99%

“…In the following, we describe what this means for a designer of a smartphone-based automotive navigation system, and review methods for estimating the orientation and position of the smartphone with respect to the vehicle. Subsequently, we [122], [140], [182] IMU, GNSS [13], [54], [183], [184] IMU, Magnetometer [185], [186] IMU, GNSS, Magnetometer [187], [188] discuss supplementary information sources and how these can be used to improve the navigation solution.…”

Section: A Navigationmentioning

confidence: 99%

“…Often, the estimation will use IMU measurements and exploit the fact that the vehicle's velocity is roughly aligned with the forward direction of the vehicle frame [13]. Moreover, the smartphone's roll and pitch angles can be estimated from accelerometer measurements during zero acceleration [175], the smartphone's yaw angle can be estimated from magnetometer measurements [168], the vehicle's roll and pitch angles can in many cases be assumed to be approximately zero [53], and the vehicle's yaw angle can be estimated from GNSS measurements of course (or consecutive GNSS measurements of position) 2 [188]. Alternatively, the smartphone-to-vehicle orientation can be estimated by applying principal component analysis to 1) accelerometer measurements [184]: most of the variance is (due to forward acceleration and braking) concentrated along the longitudinal direction of the vehicle frame, and most of the remaining variance is concentrated along the lateral direction of the vehicle frame (with large accelerations in this direction associated with vehicle turns); or 2) gyroscope measurements [181]: most of the variance is concentrated along the vehicle's yaw axis (with large angular velocities in this direction associated with vehicle turns), and most of the remaining variance is concentrated along the vehicle's pitch axis (with large angular velocities in this direction associated with changes in the inclination of the road).…”

Section: A Navigationmentioning

confidence: 99%

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Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

156

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Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

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Section: Sensorsmentioning

confidence: 99%

Section: A Navigationmentioning

confidence: 99%

Section: A Navigationmentioning

confidence: 99%

See 1 more Smart Citation

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

156

View full text Add to dashboard Cite

show abstract

“…The key point to realize the above function is to apply high precision positioning. Some researchers, such as Khaleghi et al, Yuan et al, and Tu et al, proposed to have built-in integration of multi-sensor equipment such as RTK, PPP, accelerometers, gyroscopes and magnetometers to improve the positioning accuracy and the calibration accuracy of the equipment [ 19 , 20 , 21 ]. Guo et al suggested applying wide area precise positioning in vehicle network systems and the development of a concurrent service system with a flexible virtual extension framework to ensure reliable data exchange between the vehicle and the background, so as to realize a lane level positioning service [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

A Police and Insurance Joint Management System Based on High Precision BDS/GPS Positioning

Zuo

Guo

Liu

et al. 2018

Sensors

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Car ownership in China reached 194 million vehicles at the end of 2016. The traffic congestion index (TCI) exceeds 2.0 during rush hour in some cities. Inefficient processing for minor traffic accidents is considered to be one of the leading causes for road traffic jams. Meanwhile, the process after an accident is quite troublesome. The main reason is that it is almost always impossible to get the complete chain of evidence when the accident happens. Accordingly, a police and insurance joint management system is developed which is based on high precision BeiDou Navigation Satellite System (BDS)/Global Positioning System (GPS) positioning to process traffic accidents. First of all, an intelligent vehicle rearview mirror terminal is developed. The terminal applies a commonly used consumer electronic device with single frequency navigation. Based on the high precision BDS/GPS positioning algorithm, its accuracy can reach sub-meter level in the urban areas. More specifically, a kernel driver is built to realize the high precision positioning algorithm in an Android HAL layer. Thus the third-party application developers can call the general location Application Programming Interface (API) of the original standard Global Navigation Satellite System (GNSS) to get high precision positioning results. Therefore, the terminal can provide lane level positioning service for car users. Next, a remote traffic accident processing platform is built to provide big data analysis and management. According to the big data analysis of information collected by BDS high precision intelligent sense service, vehicle behaviors can be obtained. The platform can also automatically match and screen the data that uploads after an accident to achieve accurate reproduction of the scene. Thus, it helps traffic police and insurance personnel to complete remote responsibility identification and survey for the accident. Thirdly, a rapid processing flow is established in this article to meet the requirements to quickly handle traffic accidents. The traffic police can remotely identify accident responsibility and the insurance personnel can remotely survey an accident. Moreover, the police and insurance joint management system has been carried out in Wuhan, Central China’s Hubei Province, and Wuxi, Eastern China’s Jiangsu Province. In a word, a system is developed to obtain and analyze multisource data including precise positioning and visual information, and a solution is proposed for efficient processing of traffic accidents.

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