Driving Behavior Modeling Based on Hidden Markov Models with Driver's Eye-Gaze Measurement and Ego-Vehicle Localization

Akai, Naoki; Hirayama, Takatsugu; Morales, Luis Yoichi; Akagi, Yasuhiro; Liu, Hailong; Murase, Hiroshi

doi:10.1109/ivs.2019.8814287

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…To effectively predict how a driver will react to an RtI, the ADS must understand the driver's ability to undertake it (Koesdwiady et al 2016, Yi et al 2019a. This is often accomplished via vehicle-oriented (e.g., acceleration or driving path) or driver-oriented (e.g., eye closure or hand position) approaches (Hecht et al 2018, Akai et al 2019. Given the substantial effect of driver behavior on roadway safety (Brookhuis andDe Waard 2010, Wang et al 2020), predictive models have been a focus of recent DSM research (Torres, Ohashi, and Pessin 2019;Yi et al 2019b), with a few notable examples adopting a Bayesian perspective (Agamennoni, Nieto, and Nebot 2011;Straub, Zheng, and Fisher 2014).…”

Section: Driver-state Monitoringmentioning

confidence: 99%

Managing Driving Modes in Automated Driving Systems

Insua

Caballero

Naveiro

2022

Transportation Science

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Current technology is unable to produce massively deployable, fully automated vehicles that do not require human intervention. Given that such limitations are projected to persist for decades, scenarios requiring a driver to assume control of a semiautomated vehicle, and vice versa, will remain a feature of modern roadways for the foreseeable future. Herein, we adopt a comprehensive perspective of this problem by simultaneously considering operational design domain supervision, driver and environment monitoring, trajectory planning, and driver-intervention performance assessment. More specifically, we develop a modeling framework for each of the aforementioned functions by leveraging decision analysis and Bayesian forecasting. Utilizing this framework, a suite of algorithms is subsequently proposed for driving-mode management and early warning emission, according to a management by exception principle. The efficacy of the developed methods is illustrated and examined via a simulated case study.

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Section: Driver-state Monitoringmentioning

confidence: 99%

Managing Driving Modes in Automated Driving Systems

Insua

Caballero

Naveiro

2022

Transportation Science

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“…Since HMM is sometimes inefficient because it cannot integrate past and input data, autoregressive input and output HMM (AIOHMM) were proposed to overcome the HMM limitation and obtain a better driving behaviour model based on driver visual direction, gas pedal, and steering wheel. It occurred that the AIOHMM driver's model had the best precision with five hidden states for different tasks (turn left, turn right, go straight, follow participant) [55]. However, HMM is not enough to provide a better driving prediction model for all drivers due to individual driving behaviour.…”

Section: Hidden Markov Model (Hmm) For Driving Behaviourmentioning

confidence: 99%

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

Tientcheu

Djouani

2022

Electronics

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With the availability of modern assistive techniques, ambient intelligence, and the Internet of Things (IoT), various innovative assistive environments have been developed, such as driving assistance systems (DAS), where the human driver can be provided with physical and emotional assistance. In this human–machine collaboration system, haptic interaction interfaces are commonly employed because they provide drivers with a more manageable way to interact with other components. From the view of control system theory, this is a typical closed-loop feedback control system with a human in the loop. To make such a system work effectively, both the driving behaviour factors, and the electrical–mechanical components should be considered. However, the main challenge is how to deal with the high degree of uncertainties in human behaviour. This paper aims to provide an insightful overview of the relevant work. The impact of various types of haptic assistive driving systems (haptic guidance and warning systems) on driving behaviour performance is discussed and evaluated. In addition, various driving behaviour modelling systems are extensively investigated. Furthermore, the state-of-the-art driving behaviour controllers are analysed and discussed in driver–vehicle–road systems, with potential improvements and drawbacks addressed. Finally, a prospective approach is recommended to design a robust model-free controller that accounts for uncertainties and individual differences in driving styles in a haptic assistive driving system. The outcome indicated that the haptic feedback system applied to the drivers enhanced their driving performance, lowered their response time, and reduced their mental workload compared to a system with auditory or visual signals or without any haptic system, despite some annoyances and system conflicts. The driving behaviour modelling techniques and the driving behaviour control with a haptic feedback system have shown good matching and improved the steering wheel’s base operation performance. However, mathematical principles, a statistical approach, and the lack of consideration of the individual differences in the driver–vehicle–road system make the modelling and the controller less robust and inefficient for different driving styles.

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“…For this task, the approximate direction of drivers' gaze is often used unless eye-tracking data is available as in [157]. The processing pipeline for obtaining gaze features usually includes face detection and tracking, followed by facial landmark detection, extraction of gaze zones [79], [84], [158], gaze duration, frequency, and blinks [79], [84].…”

Section: Driver Maneuver Recognition and Predictionmentioning

confidence: 99%

“…Besides discriminative models, temporal modeling that fits the data more naturally has also been applied. Jain et al [159] and Akai et al [157] propose auto-regressive input-output Hidden Markov Models (HMMs) to classify driver's actions given driver gaze and vehicle dynamics. Recurrent networks are also effective for multi-modal data [160] but lack the explainability of HMMs.…”

Section: Driver Maneuver Recognition and Predictionmentioning

confidence: 99%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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Driving safety has been a concern since the first cars appeared on the streets. Driver inattention has been singled out as a major cause of accidents early on. This is hardly surprising, as drivers routinely perform other tasks in addition to controlling the vehicle. Decades of research into what causes lapses or misdirection of drivers' attention resulted in improvements in road safety through better design of infrastructure, driver training programs, in-vehicle interfaces, and, more recently, the development of driving assistance systems (ADAS) and driving automation. This review focuses on the methods for modeling and detecting spatio-temporal aspects of drivers' attention, i.e. where and when they look, for the two latter categories of applications.We start with a brief theoretical background on human visual attention, methods for recording and measuring attention in the driving context, types of driver inattention, and factors causing it. We then discuss machine learning approaches for 1) modeling gaze for assistive and self-driving applications and 2) detecting gaze for driver monitoring. Following the overview of state-of-the-art models, we provide an extensive list of publicly available datasets that feature recordings of drivers' gaze and other attention-related annotations. We conclude with a general overview of the remaining challenges, such as data availability and quality, evaluation methods, and the limited scope of attention modeling, and outline steps toward rectifying some of these issues. Categorized and annotated lists of the reviewed models and datasets are available at https://github.com/ykotseruba/attention_and_driving

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Driving Behavior Modeling Based on Hidden Markov Models with Driver's Eye-Gaze Measurement and Ego-Vehicle Localization

Cited by 17 publications

References 16 publications

Managing Driving Modes in Automated Driving Systems

Managing Driving Modes in Automated Driving Systems

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

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