Driver Behaviour State Recognition based on Speech

Kamaruddin, Norhaslinda; Rahman, Abdul Wahab Abdul; Halim, Khairul Ikhwan Mohamad; Noh, Muhammad Hafiq Iqmal Mohd

doi:10.12928/telkomnika.v16i2.8416

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…The discriminative model, however, computes the conditional probability distribution p(B d |X ) to analyze which driving behavior class B d the input data set X belongs to. Support vector machine (SVM) [172], k-nearest neighbors (kNN) [173], fuzzy logic system (FLS) [174], logistic regression [175], conditional random fields (CRFs) [176], deep artificial neural networks (ANN) such as multi-layer perceptron (MLP) [177], convolutional neural network (CNN) [178], recurrent neural network (RNN) [179], reinforcement learning (RL) models [180] etc., are among this model. To enhance the prediction capability, some studies used hybrid techniques.…”

Section: Data-driven Behavior Modelsmentioning

confidence: 99%

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Negash

Yang

2023

IEEE Access

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Driver behavior models have been used as input to self-coaching, accident prevention studies, and developing driver-assisting systems. In recent years, driver behavior recognition has revolutionized autonomous vehicles (AVs) and traffic management studies. This comprehensive survey provides an up-todate review of the different driver behavior models and modeling approaches. In heterogeneous streets where humans and autonomous vehicles operate simultaneously, predicting the intent and action of human drivers is crucial for AVs with the help of wireless communication and artificial intelligence (AI) technologies. Therefore, the review also summarizes the applications of driver behavior modeling (DBM) for effective behavior recognition and human-like AV driving. Moreover, the review also covers the application of DBM in capturing behaviors of complex dynamic driving tasks. In this review, we solely cover car-following (CF) and lane-changing (LC) maneuvers.INDEX TERMS Driver behavior modeling (DBM), autonomous vehicles, behavior recognition, human-like, car-following, lane-changing, perception, decision-making, data-driven, vehicle control. I. INTRODUCTIONIn 2018, the US Department of Transportation reported 6,734,000 motor vehicle-related accidents. 1 Among these accidents, there were 2.71 million injuries and 36,560 fatalities. These deaths and injuries also increased in the years 2019 and 2020. Earlier in 2015, specifically in the US, 80 − 94% of crashes were due to driver-related errors, and 33% were due to the wrong prediction of other drivers [1]. The statistics of accidents caused by human driver-related errors were also reported in [2] and [3].The accidents and their behind reasons will lead us back to human drivers with rich and diverse behaviors. According to Michon [4], a human as a problem solver can be a road user, a transportation consumer, a social agent, and a psycho-biological organism. The issues to be solved are controlling a vehicle, trip-making, communication, and satisfying the basic driving necessity. A human driver can have unique traits towards vehicle controlThe associate editor coordinating the review of this manuscript and approving it for publication was Divanilson Rodrigo Campelo .

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Section: Data-driven Behavior Modelsmentioning

confidence: 99%

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Negash

Yang

2023

IEEE Access

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“…Tang et al [14] proposed a novel lane change detection mechanism called FCMNN, which is based on the combination of Fuzzy C-means clustering algorithm and adaptive neural network. Kamaruddin et al [15] detected the driver behavior based on emotions collected from speech using a combination of MFCC and MLP. Hu et al [16] identified the driver activities such as using a mobile phone, talking with passengers, and smoking from still images using a deep learning multi-stream convolutional neural network approach.…”

Section: Prior Workmentioning

confidence: 99%

Driving Style Recognition System Using Smartphone Sensors Based on Fuzzy Logic

Kalra¹,

Goyal²,

Parashar³

et al. 2021

Computers, Materials &Amp; Continua

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Every 24 seconds, someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15-29 years. 1.35 million people globally die every year due to road traffic crashes. An additional 20-50 million suffer from non-fatal injuries, often resulting in longterm disabilities. This costs around 3% of Gross Domestic Product to most countries, and it is a considerable economic loss. The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents. However, there is still no remarkable reduction in the number of accidents. To ensure driver safety and achieve vision of zero accidents, there is a great need to monitor drivers' driving styles. Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors. As most people are using smartphones in the modern era, a system based on mobile application is proposed, which can reduce the cost for developing intelligent transport systems (ITS) to a large extent. In this paper, we utilize the accelerometer sensor data and the global positioning system (GPS) sensor deployed in smartphones to recognize driving and speeding events. A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters. Thus, the proposed system uses fuzzy logic rather than taking the crisp values of the sensors. Results indicate that the proposed system can classify reckless driving based on fuzzy logic and, therefore, reduce the number of accidents.

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“…Arduino Uno can be activated via a USB connection or with an external power supply [23]. The power source is selected automatically [24]. This microcomputer has 14 digitals I/O port (six of them can provide the PWM outputs), 6 analog I/O ports, a reset switch and a ISP download [25].…”

Section: Arduino Unomentioning

confidence: 99%

Development of a Simple and Low-cost Smartphone Gimbal with MPU-6050 Sensor

Rafiq¹,

Rohman²,

Riyanto³

2020

jrc

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This paper discusses about design and development of gimbal smartphone, which are made to be simple and less money consuming by utilizing microcontroller and MPU 6050 sensor. Recently, the development of videography and image processing is inseparable from the rapid advance in smartphone technology. One of the most popular features is camera. Hand movements and shocks cause fewer maximum results. To improve the quality of camera captures and video, it is necessary to have a stabilizer for camera position. Thus, it is expected that the outcomes of this paper are able to give contribution for cheap gimbal smartphone. Design and implementation of gimbal uses acrylic as the material, with 5 mm of thickness. This MPU 6050 sensor is optimized to detect sway in axis X, Y, and Z or roll, pitch, and yaw. Gyroscope and accelerometer provide input to the microcontroller, which will process for output on 3 servomotors that function to maintain the camera's position at a specified set point. The results show that MPU 6050 sensor can response angle reading error of 1.34° of roll, 0.25° of pitch, and 0.78° of yaw. Error in maximum servomotor movement is 1.5°. Thus, it can be concluded that gimbal can work optimally with less money consuming as well as low error. It is expected that the next research will add other suitable and accurate control, that is, PID or fuzzy.

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Driver Behaviour State Recognition based on Speech

Abstract: Abstract

Cited by 6 publications

References 9 publications

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Driving Style Recognition System Using Smartphone Sensors Based on Fuzzy Logic

Development of a Simple and Low-cost Smartphone Gimbal with MPU-6050 Sensor

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