AutoRate: How attentive is the driver?

Dua, Isha; Nambi, Akshay; Jawahar, C. V.; Padmanabhan, Venkat

doi:10.1109/fg.2019.8756620

Cited by 22 publications

(18 citation statements)

References 33 publications

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“…In the area of managing the energy consumption of buildings, detecting the number of occupants through human sensing has been a primary target of many energy-saving techniques, such as correlating electrical load usage with occupancy sensors [3,43]. In automotive applications, the human driving behavior has been integrated into the loop of computation of many Advanced Driver Assistance Systems (ADAS) [11,36], such as activate the automatic cruise control [33] or adjust the threshold of the forward collision warning [14]. In smart cities, human tracking is used for dynamic resource allocation of community services [50].…”

Section: Related Workmentioning

confidence: 99%

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FaiR-IoT

Elmalaki

2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

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Thanks to the rapid growth in wearable technologies, monitoring complex human context becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing such personalized IoT applications arises from human variability. Such variability stems from the fact that different humans exhibit different behaviors when interacting with IoT applications (intra-human variability), the same human may change the behavior over time when interacting with the same IoT application (inter-human variability), and human behavior may be affected by the behaviors of other people in the same environment (multi-human variability). To that end, we propose FaiR-IoT, a general reinforcement learning-based framework for adaptive and fairness-aware human-in-the-loop IoT applications. In FaiR-IoT, three levels of reinforcement learning agents interact to continuously learn human preferences and maximize the system's performance and fairness while taking into account the intra-, inter-, and multi-human variability. We validate the proposed framework on two applications, namely (i) Human-in-the-Loop Automotive Advanced Driver Assistance Systems and (ii) Human-in-the-Loop Smart House. Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%−60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1.5 orders of magnitude. CCS Concepts• Computing methodologies → Multi-agent reinforcement learning; • Human-centered computing → Ubiquitous computing; Collaborative and social computing.

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Section: Related Workmentioning

confidence: 99%

“…The sampling time is fixed at 0.25 seconds while and are multiples of the sampling time. In particular, we choose ∈ [80, 90, 100, 110] and ∈ [8,9,10,11,12,13,14,15] multiples of . A state is one combination of and , such as (80,8).…”

Section: Implementation and Evaluation Planmentioning

confidence: 99%

FaiR-IoT

Elmalaki

2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

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“…They take photos, selfies, record voice and sounds. These signals are being used for a wide range of research lines: user recognition [31], emotion aware [32], driver attention [33], pain detection [34], face tracking [35], heart rate estimation [36], environmental sound recognition [37], and speech recognition [38] among others. However, their capacity to collect private user information can be perceived as intrusive.…”

Section: Mobile Sensors For Modeling Human-machine Interactionmentioning

confidence: 99%

Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication

Acien

Morales

Vera-Rodríguez

et al. 2020

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)

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In this paper we list the sensors commonly available in modern smartphones and provide a general outlook of the different ways these sensors can be used for modeling the interaction between human and smartphones. We then provide a taxonomy of applications that can exploit the signals originated by these sensors in three different dimensions, depending on the main information content embedded in the signals exploited in the application: neuromotor skills, cognitive functions, and behaviors/routines. We then summarize a representative selection of existing research datasets in this area, with special focus on applications related to user authentication, including key features and a selection of the main research results obtained on them so far. Then, we perform the experimental work using the HuMIdb database (Human Mobile Interaction database), a novel multimodal mobile database that includes 14 mobile sensors captured from 600 participants. We evaluate a biometric authentication system based on simple linear touch gestures using a Siamese Neural Network architecture. Very promising results are achieved with accuracies up to 87% for person authentication based on a simple and fast touch gesture.

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“…The vast majority of vehicle crashes is due to driver's inattention [1]. Thus, the phenomenon has become a research problem referred to as DADA, driver attention prediction in driving accident scenario [2,3].…”

Section: Introductionmentioning

confidence: 99%

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

et al. 2021

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Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

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AutoRate: How attentive is the driver?

Cited by 22 publications

References 33 publications

FaiR-IoT

FaiR-IoT

Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

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