Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Saenz, Humberto; Sun, Huiming; Wu, Lingtao; Zhou, Xuesong; Yu, Hongkai

doi:10.1049/itr2.12012

Cited by 10 publications

(3 citation statements)

References 36 publications

(79 reference statements)

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“…Rangesh et al [23] show the practical importance of having gaze annotations, both for eye contact between drivers and pedestrians, or phone-related distractions. Going further to recognize actions implying a phone, Saenz et al [24] use a two-branch convolutional network to predict distracted behaviors due to phone usage from stereo image pairs. While detecting phone-related activities or pedestrian intentions are crucial tasks, eye-contact detection remains an essential channel of communication.…”

Section: B Eye Contact Between Pedestrians and Vehiclesmentioning

confidence: 99%

Do Pedestrians Pay Attention? Eye Contact Detection in the Wild

Belkada¹,

Bertoni²,

Caristan³

et al. 2021

Preprint

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In urban or crowded environments, humans rely on eye contact for fast and efficient communication with nearby people. Autonomous agents also need to detect eye contact to interact with pedestrians and safely navigate around them. In this paper, we focus on eye contact detection in the wild, i.e., real-world scenarios for autonomous vehicles with no control over the environment or the distance of pedestrians.We introduce a model that leverages semantic keypoints to detect eye contact and show that this high-level representation (i) achieves state-of-the-art results on the publicly-available dataset JAAD, and (ii) conveys better generalization properties than leveraging raw images in an end-to-end network. To study domain adaptation, we create LOOK: a large-scale dataset for eye contact detection in the wild, which focuses on diverse and unconstrained scenarios for real-world generalization. The source code and the LOOK dataset are publicly shared towards an open science mission.

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Section: B Eye Contact Between Pedestrians and Vehiclesmentioning

confidence: 99%

Do Pedestrians Pay Attention? Eye Contact Detection in the Wild

Belkada¹,

Bertoni²,

Caristan³

et al. 2021

Preprint

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show abstract

“…It can be applied in signalized interfaces to proactively detect incipient congestion and set the best cycle and phases of traffic lights [6]. Deep learning is used for crowdedness prediction [7], short-term prediction of traffic flow [8], pedestrian behavior recognition [9,10], driving policy for autonomous road vehicles [11], and license plate segmentation and recognition [12][13][14]. In addition, mobile-edge computing [15], railway traffic conflict control [16], and federal learning [17] have also been applied to transportation.…”

Section: Introductionmentioning

confidence: 99%

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Chen

Cai

Huang

et al. 2021

Scientific Programming

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To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k1 of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into “hot technology,” “cold technology,” and “other technologies,” according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k2. We use the Gaussian mixture model (GMM) algorithm to cluster k1 and k2 of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies.

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“…erefore, it is particularly meaningful to study the drivers' perception abilities (driver physiological measures, subjective evaluation performance, etc.) and driving performance parameters during distracted driving and extract the parameters that are significantly different from those of normal driving to detect the distracted state of drivers [8,9]. e physiological characteristics of drivers are a major aspect of distracted driving recognition that many researchers have utilized, and many studies have indicated that the effects of distracted driving on the pupil diameter, fixation angle, and blink frequency can be verified [10]; thus, these parameters can be used as cognitive distraction recognition features [11,12].…”

Section: Introductionmentioning

confidence: 99%

Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments

Hua

Jin

Jiang

et al. 2021

Journal of Advanced Transportation

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Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.

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Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Cited by 10 publications

References 36 publications

Do Pedestrians Pay Attention? Eye Contact Detection in the Wild

Do Pedestrians Pay Attention? Eye Contact Detection in the Wild

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments

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