Driver’s Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks

Liu, Yaqi; Wang, Xiaoyuan; Chen, Longfei; Liu, Shijie; Han, Junyan; Shi, Huili; Zhong, Fusheng

doi:10.3390/ijerph19095059

Cited by 6 publications

(1 citation statement)

References 53 publications

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“…The attention mechanism can be seen as a simulation of human attention; that is, humans can pay attention to valuable information, while ignoring useless information. A new attention mechanism architecture, a transformer based on the self-attention Seq2seq [28] model, has demonstrated powerful capabilities in sequential data processing, such as natural language processing [29], audio processing [30], and even computer vision [31]. Unlike a RNN, transformer allows the model to access any part of history without considering the distance, making it more suitable for mastering repeated patterns with long-term dependencies and for preventing overfitting.…”

Section: Introductionmentioning

confidence: 99%

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

Jin,

Chen,

et al. 2023

Sensors

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GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

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Section: Introductionmentioning

confidence: 99%

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

Jin,

Chen,

et al. 2023

Sensors

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Driving emotions: using virtual reality to explore the effect of low and high arousal on driver’s attention

Dozio,

Bertoni,

Ferrise

2024

Virtual Reality

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The role played by emotions and attention is crucial for the development of advanced driver assistance systems that improve safety by flexibly adapting to the current state of the driver. In the present study, we used immersive virtual reality as a testing tool to investigate how different emotional states affect drivers’ attention in a divided attention task. Two different emotional states, diversified by valence and arousal, were induced before performing a divided attention task in a driving simulation. The experimental task developed for this study allowed us to explore if and how two different emotional states can affect the way drivers divide their attention between a central driving-related task and a peripheral visual task. Our results showed that scared drivers presented lower reaction times at the central task compared to relaxed drivers. On the contrary, the emotional state did not affect the performance at the peripheral task, which revealed instead a significant effect of the eccentricity at which the visual stimuli were presented, influencing both the accuracy of targets’ perception and participants’ reaction times.

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How does a driver feel behind the wheel? An exploratory study of drivers’ emotions and the effect of their sociodemographic background

Sheikholeslami

Saffarzadeh

Mamdoohi

et al. 2023

Accident Analysis & Prevention

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Driver’s Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks

Cited by 6 publications

References 53 publications

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

Driving emotions: using virtual reality to explore the effect of low and high arousal on driver’s attention

How does a driver feel behind the wheel? An exploratory study of drivers’ emotions and the effect of their sociodemographic background

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