Driver Distraction Detection Using Semi-Supervised Machine Learning

Liu, Tianchi; Yan, Yang; Huang, Guang-Bin; Yeo, Yong Kiang; Lin, Zhiping

doi:10.1109/tits.2015.2496157

Cited by 186 publications

(63 citation statements)

References 35 publications

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“…Prior research [4]- [9] has addressed the closely related problem of estimating driver distraction under manual driving conditions. Driver distraction has been defined as the diversion of the driver's attention away from activities critical for safe The driving toward a competing activity, which may result in insufficient or no attention to activities critical for safe driving [10].…”

Section: Introductionmentioning

confidence: 99%

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

Deo¹,

Trivedi²

2020

IEEE Trans. Intell. Veh.

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Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this paper, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from invehicle vision sensors. We present an extensive naturalistic drive dataset of drivers in a conditionally autonomous vehicle running on Californian freeways. We collect subjective ratings for the driver's take-over readiness from multiple human observers viewing the sensor feed. Analysis of the ratings in terms of intra-class correlation coefficients (ICCs) shows a high degree of consistency in the ratings across raters. We define a metric for the driver's take-over readiness termed the 'Observable Readiness Index (ORI)' based on the ratings. Finally, we propose an LSTM model for continuous estimation of the driver's ORI based on a holistic representation of the driver's state, capturing gaze, hand, pose and foot activity. Our model estimates the ORI with a mean absolute error of 0.449 on a 5 point scale.

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Section: Introductionmentioning

confidence: 99%

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

Deo¹,

Trivedi²

2020

IEEE Trans. Intell. Veh.

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“…Therefore, to deal with this problem, two approaches are emerging from the literature: (1) unsupervised or semi-supervised learning and (2) automatic data reduction. For example, in connection with the first approach, Liu et al [189] commented the benefits of SSL methods. Specifically, the explained the benefits of using SSL increased with the size of unlabeled data set showing that by exploring the data structure without actually labeling them, extra information to improve models performance can be obtained.…”

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

“…SS-ELM based detection system has the potential of improving accuracy and alleviating the cost of adapting distraction detection systems to new drivers, and thus, more promising for real world applications. However, several points are unclear from these preliminary results [12] further explored in [189], where the Semi-Supervised Learning (SSL) paradigm is introduced to real time detection of distraction based on eye and head movements.…”

Section: Cognitive Distractionmentioning

confidence: 99%

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Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

104

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…Moreover, various research works [8,9,10] have implemented machine learning algorithms to detect critical driving events such as hard braking and sudden acceleration. These approaches are able to provide continuous quality improvement in the results.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Data-Intensive Telematics Functionalities at the Extreme Edge of the Network: Experience with the PrEstoCloud Project

Taherizadeh

Novak

Komatar

et al. 2018

2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)

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Abstract-In recent years, use of different sensors connected to vehicles is dramatically increasing in order to enhance transportation efficiency. The current Big Data technologies are predominantly used to store large amount of telematics data especially in the cloud, and they are only able to perform simple querying for the purpose of reporting. While all the data is stored in the cloud-centric datacenters, these telematics systems are not capable of exploiting other functionalities offered by advanced real-time analytics such as run-time anomaly detection. In this paper, we propose an advanced telematics system orchestrated upon an edge computing framework in the context of the PrEstoCloud (Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing) project. This telematics system is a real-time data-intensive application running at the extreme edge of the network for drivers' behavior profiling and triggering run-time alerts. Such functionalities may be useful in order to notify stakeholders for example drivers and logistic centers on situations where a possible accident may occur or attention is required.

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Driver Distraction Detection Using Semi-Supervised Machine Learning

Cited by 186 publications

References 35 publications

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

Driver Distraction Using Visual-Based Sensors and Algorithms

Real-Time Data-Intensive Telematics Functionalities at the Extreme Edge of the Network: Experience with the PrEstoCloud Project

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