All In One Network for Driver Attention Monitoring

Li, Xinlei; Dai, Xiaotian; Zhang, Rui; Qi, Lizhe; Zhang, Wenqiang; Jiang, Zhe

doi:10.1109/icassp40776.2020.9053659

Cited by 27 publications

(9 citation statements)

References 19 publications

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“…The underlying essence for the HVI study is to understand both drivers' states and the temporal events outside the vehicle, by leveraging multiple data-driven techniques. Current data-driven techniques are designed for various purposes, such as (1) tracking driver attention [6,49]; (2) analyzing driving styles [22,33,46]; (3) analyzing effects of situations outside the vehicle [15,38,50];(4) predicting drivers' drowsiness, attention, and emotion through facial expressions or eye gazes [14,26,34,35]; (5) monitoring driver's behaviors regarding distractions and temporal performance by utilizing body-based monitoring mechanisms [1,8]; (6) detecting drivers' emotion arousal (e.g. neutral, sadness and happiness) [16,28,36].…”

Section: Data-driven Techniques For Human-vehicle Interactionmentioning

confidence: 99%

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Jin¹,

Duan²,

Liu³

et al. 2022

Preprint

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Emerging Autonomous Vehicles (AV) breed great potentials to exploit data-driven techniques for adaptive and personalized Human-Vehicle Interactions. However, the lack of high-quality and rich data supports limits the opportunities to explore the design space of data-driven techniques, and validate the effectiveness of concrete mechanisms. Our goal is to initialize the efforts to deliver the building block for exploring data-driven Human-Vehicle Interaction designs. To this end, we present BROOK dataset, a multi-modal dataset with facial video records. We first brief our rationales to build BROOK dataset. Then, we elaborate how to build the current version of BROOK dataset via a year-long study, and give an overview of the dataset. Next, we present three example studies using BROOK to justify the applicability of BROOK dataset. We also identify key learning lessons from building BROOK dataset, and discuss about how BROOK dataset can foster an extensive amount of follow-up studies. INTRODUCTIONRecent advances in data-driven techniques (e.g. Neural Networks) breed an extensive amount of opportunities, to enable adaptive and personalized Human-Vehicle Interaction (HVI). More interestingly, the emerging trends of Autonomous Vehicles relax the conventional burdens of driving, and in-vehicle drivers/passengers are capable to obtain better user experiences through more complex HVI. Incorporating data-driven approaches can greatly improve user experiences during driving processes. For instance, unobtrusive monitors of multi-modal statuses (i.e. by taking alternative data sources as input), rather than directly equipping biosensors, are more user-friendly as data sources for personalized interaction between drivers and vehicles. Therefore, combining data-driven techniques with HVI becomes promising in the near future, and relevant datasets become essential and highly demanded.Though there are already several datasets for HVI, there are four major limitations of existing datasets. First, the design purposes of existing datasets focus on a specific type of study purposes (e.g. Stress Detection [20], Driving Workload [40] and etc.), which naturally limits their potentials due to the narrowly-selected types of data streams. Second, existing datasets only collect information

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Section: Data-driven Techniques For Human-vehicle Interactionmentioning

confidence: 99%

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

Jin¹,

Duan²,

Liu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, an important task includes detecting the state of people within a vehicle, to avoid negative states and seek to achieve positive states. Negative states can involve sleepiness, distraction, drunkenness, health problems (e.g., epilepsy), and negative moods (angry, fearful, and embarrassed), and individual predilections (some drivers can prefer to drive more wildly or be unsure how to interpret some driving situations due to lack of experience) [124]. Positive states can involve comfort and enjoyment [125].…”

Section: Driver Monitoringmentioning

confidence: 99%

AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control

et al. 2021

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Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

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“…Thus, an important task includes detecting the state of people within a vehicle, to avoid negative states and seek to achieve positive states. Negative states can involve sleepiness, distraction, drunkenness, health problems (e.g., epilepsy), and negative affect (anger, fear, and embarrassment), as well as individual predilections (some drivers can prefer to drive more wildly, or be unsure how to interpret some driving situations due to lack of experience) (Yang et al, 2020). Positive states can involve comfort and enjoyment (Beggiato et al, 2020).…”

Section: Driver Monitoringmentioning

confidence: 99%

AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control

Englund¹,

Aksoy²,

Alonso‐Fernandez³

et al. 2021

Preprint

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Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, Smart Traffic Control and Driver Modelling are described along with open research challenges and standardization to help introduce advanced driver assistance systems in traffic. AI technologies provide accurate prediction and classification; however, the models do not provide any evidence on their output making them hard to trust for a human operator. In addition, there are currently no methods that can be used to describe requirements of how the data should be annotated in order to train an accurate model.

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All In One Network for Driver Attention Monitoring

Cited by 27 publications

References 19 publications

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

BROOK Dataset: A Playground for Exploiting Data-Driven Techniques in Human-Vehicle Interactive Designs

AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control

AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control

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