Driving Performance Under Violations of Traffic Rules: Novice vs. Experienced Drivers

Xu, Jiawei; Guo, Kun; Sun, Poly Z. H.

doi:10.1109/tiv.2022.3200592

Cited by 77 publications

(19 citation statements)

References 16 publications

(16 reference statements)

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“…Central fixation bias, which is a key factor in driving tasks as evidenced in Xu et al., 4 particularly for experienced drivers, is not thoroughly reflected in previous research. It is reported that human vision exerts a tendency of central fixation bias to actively search the gist of approaching scenes and bind visual features for higher cognitive understanding; that is, the visual information from the central visual field/region is paid more attention while that from outer regions is paid less attention.…”

Section: Human Factors In Drivingmentioning

confidence: 81%

Bio-inspired vision mimetics toward next-generation collision-avoidance automation

Guo

Park

et al. 2023

The Innovation

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Section: Human Factors In Drivingmentioning

confidence: 81%

Bio-inspired vision mimetics toward next-generation collision-avoidance automation

Guo

Park

et al. 2023

The Innovation

Self Cite

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“…A negative skew indicates that the left tail is longer, while a positive skew indicates a longer right tail. The mathematical equation for skewness ( Yu et al, 2021 ; Zhang et al, 2021 ; Qi et al, 2022 ; Zheng et al, 2023c ) is

where

are the individual sample points,

is the sample mean, s is the standard deviation ( Liu et al, 2021 ; J, X. et al, 2022; Mao et al, 2022b ; Guo et al, 2022 ; Xu et al, 2022b ), and N is the number of samples. Figure 7 shows skewness for different activities over both datasets.…”

Section: Methodsmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

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Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

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“…The extraction of LPCCs from accelerometer signals [67], the primary step involves the application of linear predictive analysis (LPA). Given 𝑠(𝑛) as the accelerometer signal, it can be modeled by the relation…”

Section: Linear Prediction Cepstral Coefficients (Lpccs)mentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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Driving Performance Under Violations of Traffic Rules: Novice vs. Experienced Drivers

Cited by 77 publications

References 16 publications

Bio-inspired vision mimetics toward next-generation collision-avoidance automation

Bio-inspired vision mimetics toward next-generation collision-avoidance automation

Robust human locomotion and localization activity recognition over multisensory

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

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