Vehicle Turning Behavior Modeling at Conflicting Areas of Mixed-Flow Intersections Based on Deep Learning

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

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Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Sensors ◽

10.3390/s21134592 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4592

Author(s):

Xin Zeng ◽

Xiaomei Zhang ◽

Shuqun Yang ◽

Zhicai Shi ◽

Chihung Chi

Keyword(s):

Deep Learning ◽

Mobile Devices ◽

User Authentication ◽

False Positive Rate ◽

True Positive Rate ◽

Edge Computing ◽

Behavior Modeling ◽

Security And Privacy ◽

Positive Rate ◽

Edge Based

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

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Teenage Driving Behavior Modeling Using Deep Learning for Driver Behavior Classification

Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017) ◽

10.1007/978-981-13-7279-7_56 ◽

2019 ◽

pp. 449-455 ◽

Cited By ~ 1

Author(s):

Muhamad-Husaini Abu-Bakar ◽

Rizal Razuwan ◽

Syafiq Kamal

Keyword(s):

Deep Learning ◽

Driver Behavior ◽

Driving Behavior ◽

Behavior Modeling

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A Deep Learning-Based Framework for Human Activity Recognition in Smart Homes

Mobile Information Systems ◽

10.1155/2021/6961343 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Alaeddine Mihoub

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Computational Models ◽

Ambient Assisted Living ◽

Smart Homes ◽

Feature Reduction ◽

Behavior Modeling ◽

Learning Techniques ◽

Selection Approach ◽

Reduction Approach

Human behavior modeling in smart environments is a growing research area treating several challenges related to ubiquitous computing, pattern recognition, and ambient assisted living. Thanks to recent progress in sensing devices, it is now possible to design computational models able of accurate detection of residents’ activities and daily routines. For this goal, we introduce in this paper a deep learning-based framework for activity recognition in smart homes. This framework proposes a detailed methodology for data preprocessing, feature mining, and deep learning techniques application. The novel framework was designed to ensure a deep exploration of the feature space since three main approaches are tested, namely, the all-features approach, the selection approach, and the reduction approach. Besides, the framework proposes the evaluation and the comparison of several well-chosen deep learning techniques such as autoencoder, recurrent neural networks (RNN), and some of their derivatives models. Concretely, the framework was applied on the “Orange4Home” dataset which represents a recent dataset specially designed for smart homes research. Our main findings show that the best approach for efficient classification is the selection approach. Furthermore, our overall results outperformed baseline models based on random forest classifiers and the principal component analysis technique, especially the results of our RNN-based model for the all-features approach and the results of our autoencoder-based model for the feature reduction approach.

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