This Is Me: Using Ambient Voice Patterns for In-Car Positioning

Feld, Michael S.; Schwartz, Tim; Müller, Christian

doi:10.1007/978-3-642-16917-5_33

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…It employs the smartphone embedded microphone(s) to establish the phone (owner) in-vehicle position. It uses audio signals, of inaudible frequencies, emitted from several speakers of the vehicle stereo system, possibly in a programmed sequence [39], [40]. Such signals can be instigated by the smartphone, e.g.…”

Section: E Audio Rangingmentioning

confidence: 99%

“…Other similar, basic, techniques measure the magnitude of emitted inaudible beeps by a given speaker [14], [20] or the vehicles turn signal [14], to establish the phone proximity to the audio signal source. In [40], a related seamless-voice-recognition approach is proposed.…”

Section: E Audio Rangingmentioning

confidence: 99%

“…Whilst audio-ranging-based user identification systems are in general accurate and robust [39], [40], they necessitate changing the vehicle firmware and recording audio in an unsupervised manner, which can raise privacy concerns.…”

Section: E Audio Rangingmentioning

confidence: 99%

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Driver and Passenger Identification From Smartphone Data

Ahmad

Langdon

Liang

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.

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Section: E Audio Rangingmentioning

confidence: 99%

Section: E Audio Rangingmentioning

confidence: 99%

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Driver and Passenger Identification From Smartphone Data

Ahmad

Langdon

Liang

et al. 2019

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…This of course assumes that each smartphone already has been associated with its owner's identifying characteristics. One way to implement a system of this kind is to record speech using directional microphones attached to each seat [195]. Each smartphone can then compare the recorded audio with its own unique pre-stored voice signature, and can thereby identify the seat of the smartphone owner.…”

Section: Sensorsmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

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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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“…Meanwhile, sound waves can be easily affected by other in-vehicle sound, like engine noise. Yet, another approach, “this is me” [ 6 ], identifies a phone's position based on voice patterns (a.k.a. the voiceprint) of passengers to provide different multimedia services for passengers.…”

Section: Related Workmentioning

confidence: 99%

Who Sits Where? Infrastructure-Free In-Vehicle Cooperative Positioning via Smartphones

Cao

et al. 2014

Sensors

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Seat-level positioning of a smartphone in a vehicle can provide a fine-grained context for many interesting in-vehicle applications, including driver distraction prevention, driving behavior estimation, in-vehicle services customization, etc. However, most of the existing work on in-vehicle positioning relies on special infrastructures, such as the stereo, cigarette lighter adapter or OBD (on-board diagnostic) adapter. In this work, we propose iLoc, an infrastructure-free, in-vehicle, cooperative positioning system via smartphones. iLoc does not require any extra devices and uses only embedded sensors in smartphones to determine the phones' seat-level locations in a car. In iLoc, in-vehicle smartphones automatically collect data during certain kinds of events and cooperatively determine the relative left/right and front/back locations. In addition, iLoc is tolerant to noisy data and possible sensor errors. We evaluate the performance of iLoc using experiments conducted in real driving scenarios. Results show that the positioning accuracy can reach 90% in the majority of cases and around 70% even in the worst-cases.

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This Is Me: Using Ambient Voice Patterns for In-Car Positioning

Cited by 8 publications

References 3 publications

Driver and Passenger Identification From Smartphone Data

Driver and Passenger Identification From Smartphone Data

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Who Sits Where? Infrastructure-Free In-Vehicle Cooperative Positioning via Smartphones

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