Automated safety vehicle stop system for cardiac emergencies

Takahashi, Issey; Kanamori, Hitoshi; Tanaka, Takahiro; Kato, Shigeo; Ninomiya, Yoshiki; Takeuchi, Eijiro; Nakagawa, Tsuyoshi; Makiguchi, Minoru; Aoki, Hirofumi

doi:10.1109/emergitech.2016.7737302

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Various biomarkers and physiological measurements have been proposed as objective metrics to evaluate the physical and cognitive state of human beings, including electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG), electrodermal activity (EDA), photoplethysmography (PPG), and skin temperature. The ECG, EEG, and EMG signals are measurements used to evaluate and monitor the function of the heart [ 7 , 8 ], brain [ 9 , 10 ], and muscles [ 11 , 12 ], respectively. EDA measures the variation in the skin conductivity caused by sweat secretion [ 13 , 14 , 15 ], whereas PPG is a measure used to detect the change in the blood volume using optical devices [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Steering Sleeve (S3): A Non-Intrusive and Integrative Sensing Platform for Driver Physiological Monitoring

et al. 2022

Sensors

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Driving is a ubiquitous activity that requires both motor skills and cognitive focus. These aspects become more problematic for some seniors, who have underlining medical conditions and tend to lose some of these capabilities. Therefore, driving can be used as a controlled environment for the frequent, non-intrusive monitoring of bio-physical and cognitive status within drivers. Such information can then be utilized for enhanced assistive vehicle controls and/or driver health monitoring. In this paper, we present a novel multi-modal smart steering sleeve (S3) system with an integrated sensing platform that can non-intrusively and continuously measure a driver’s physiological signals, including electrodermal activity (EDA), electromyography (EMG), and hand pressure. The sensor suite was developed by combining low-cost interdigitated electrodes with a piezoresistive force sensor on a single, flexible polymer substrate. Comprehensive characterizations on the sensing modalities were performed with promising results demonstrated. The sweat-sensing unit (SSU) for EDA monitoring works under a 100 Hz alternative current (AC) source. The EMG signal acquired by the EMG-sensing unit (EMGSU) was amplified to within 5 V. The force-sensing unit (FSU) for hand pressure detection has a range of 25 N. This flexible sensor was mounted on an off-the-shelf steering wheel sleeve, making it an add-on system that can be installed on any existing vehicles for convenient and wide-coverage driver monitoring. A cloud-based communication scheme was developed for the ease of data collection and analysis. Sensing platform development, performance, and limitations, as well as other potential applications, are discussed in detail in this paper.

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Section: Introductionmentioning

confidence: 99%

Smart Steering Sleeve (S3): A Non-Intrusive and Integrative Sensing Platform for Driver Physiological Monitoring

et al. 2022

Sensors

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“…[4], [5]), including incapacitation of the responsible human driver for systems up to SAE level 3 (e.g. [6], [7]).…”

Section: Introductionmentioning

confidence: 99%

Realtime Global optimization of a Fail-Safe Emergency Stop Maneuver for Arbitrary Electrical/ Electronical Failures in Automated Driving

Duerr

Ziehn

Kohlhaas

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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In the event of a critical system failures in automated vehicles, fail-operational or fail-safe measures provide minimum guarantees for the vehicle's performance, depending on which of its subsystems remain operational. Various such methods have been proposed which, upon failure, use different remaining sets of operational subsystems to execute maneuvers that bring the vehicle into a safe state under different environmental conditions. One particular such method proposes a fail-safe emergency stop system that requires no particular electric or electronic subsystem to be available after failure, and still provides a basic situation-dependent emergency stop maneuver. This is achieved by preemptively setting parameters to a hydraulic / mechanical system prior to failure, which after failure executes the preset maneuver "blindly". The focus of this paper is the particular challenge of implementing a lightweight planning algorithm that can cope with the complex uncertainties of the given task while still providing a globallyoptimal solution at regular intervals, based on the perceived and predicted environment of the automated vehicle.

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