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Articles de revues sur le sujet « Car driving »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 25 juillet 2025

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1

T., Dr Manikandan. "Self Driving Car." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 380–88. http://dx.doi.org/10.37200/ijpr/v24i5/pr201704.

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Blevis, Eli. "Selfish-driving car." Interactions 24, no. 2 (2017): 88. http://dx.doi.org/10.1145/3047404.

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Tamil Selvan B and Srirangarajalu N. "Self-Driving Car." international journal of engineering technology and management sciences 7, no. 4 (2023): 275–80. http://dx.doi.org/10.46647/ijetms.2023.v07i04.038.

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An autonomous vehicle is a self-driving vehicle that uses various sensors, cameras, and advanced algorithms to sense the surrounding environment, make decisions and navigate without human intervention. These vehicles are rapidly evolving and have the potential to revolutionize the transportation industry by offering increased safety, efficiency, and accessibility. Autonomous vehicles are expected to bring significant benefits such as reduced traffic congestion, lower emissions, and increased mobility for people who cannot drive. However, there are still many challenges that need to be addresse

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Patinge, Sushant. "Self-Driving Car." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 240–44. http://dx.doi.org/10.22214/ijraset.2023.50830.

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Abstract: The evolution of Internet of Things has served up the catalyst in the field of technology. Automobile manufacturers such as Ford, Audi, Hyundai, Tesla, and other companies are investing billions of dollars in autonomous vehicle driving research. According to the new information, in the next 30 years, this fast-developing industry will be worth $ 7 trillion. This will create a shift on the way cities are planned, as less parking spots will be needed, and secondly, in a developed city with most of its vehicles being connected and autonomous, traffic optimization will be able to be stro

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Asati, Sudiksha. "Self Driving Car." International Journal of Science, Engineering and Technology 12, no. 2 (2024): 1–6. http://dx.doi.org/10.61463/ijset.vol.12.issue2.138.

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Dubey, Ashutosh, and Prabakaran N. "SELF-DRIVING CAR SIMULATION." International Research Journal of Computer Science 07, no. 05 (2020): 66–69. http://dx.doi.org/10.26562/irjcs.2020.v0705.002.

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Phansekar, Soham. "LIDAR Self Driving Car." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1334–37. http://dx.doi.org/10.22214/ijraset.2021.38621.

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Abstract: Increasing population is the major issue of transportation nowadays. People who live and work in the major cities of the world are faced with increasing levels of congestion, delays, total travel time, costs, frustration, accidents and loss of life. The objective of this project is to help prevent traffic accidents and save people’s time by fundamentally changing car use. The system would have sensors to detect the obstacles and to be able to react according to their position. In this project we have developed an automated driving system which drives the car automatically. We have de

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M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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Thakur, Deepak Kumar. "Automatic Self Driving Car." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30742.

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This project deals with building an autonomous car that can travel safely and intelligently avoiding the risk of human errors. This raspberry Pi based project can detect the obstacles & traffic light. It can compare the data processed with the data provided to it and is able to take an intelligent decision whether to stop or continue its present path. Important components involved in this project are - the hardware platform which includes raspberry pi board, all the hardware like pi camera and the ultrasonic sensor for improved efficiency & the camera used along with an ultrasonic sens

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Ashutosh, Dubey, and N. Prabakaran. "SELF-DRIVING CAR SIMULATION." International Research Journal of Computer Science VII, no. V (2020): 66–69. https://doi.org/10.26562/irjcs.2020.v0705.002.

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For the recent years, there has been a flood of enthusiasm for self-driving vehicles. This is because of forward leaps in the field of deep learning where deep neural networks are trained to perform tasks that usually require human intervention.CNN apply models to distinguish examples and highlights in pictures, making them helpful in the field of Computer Vision. Instances of these are object detection, image classification, image captioning, etc. In this project, we have prepared a CNN utilizing pictures captured by a simulated vehicle so as to drive the vehicle self-sufficiently. The CNN le

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Vijay, Laxmi Kalyani, and Pinky. "The Study of Self Driven Cars With Special Reference to Google Drive And Maruti Celerio In High Speed Internet Connectivity." Journal of Management Engineering and Information Technology (JMEIT) 4, no. 6 (2017): 12–18. https://doi.org/10.5281/zenodo.1134249.

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The automobile industries in India growing very rapidly. Nowadays automobile companies are working on self drive car but the Indian roads not competable to driven self driving car on the roads therefore new Aerodrive Celerio self driving car is the best solution to overcome this problem. Maruti Celerio self driving car is totally made up wires and automatic features. It is a crude feature. In India it is developed by small startup called fisheyebox is the first. The basic concept of car is self drive and work by computer system/ features , with the help of camera, sensor , radar ,electricity.

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Zhao, Jianfeng, Bodong Liang, and Qiuxia Chen. "The key technology toward the self-driving car." International Journal of Intelligent Unmanned Systems 6, no. 1 (2018): 2–20. http://dx.doi.org/10.1108/ijius-08-2017-0008.

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Purpose The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue. Design/methodology/approach This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-drivin

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Kumar, Dr S. Senthil, Anjali S, and Hashina Parveen S. Aishwarya R. "Automatic Car Window Opener for safe Driving." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1253–56. http://dx.doi.org/10.31142/ijtsrd9606.

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Kerr, Sophie-May, Natascha Klocker, and Gordon Waitt. "Diverse Driving Emotions." Transfers 8, no. 2 (2018): 23–43. http://dx.doi.org/10.3167/trans.2018.080203.

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In the industrialized West, cars are considered an essential part of everyday life. Their dominance is underpinned by the challenges of managing complex, geographically stretched daily routines. Drivers’ emotional and embodied relationships with automobiles also help to explain why car cultures are difficult to disrupt. This article foregrounds ethnic diversity to complicate notions of a “love affair” with the car. We report on the mobilities of fourteen Chinese migrants living in Sydney, Australia—many of whom described embodied dispositions against the car, influenced by their life histories

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Koma, Hiroaki, Taku Harada, Akira Yoshizawa, and Hirotoshi Iwasaki. "Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 1 (2019): 13–24. http://dx.doi.org/10.4018/ijcini.2019010102.

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The effectiveness of considering the ambient state of a driving car for evaluating the driver's cognitive distracted state is evaluated. In this article, Support Vector Machines and Random Forest, which are representative machine learning models, are applied. As input data for the machine learning model, in addition to a driver's biometric data and car driving data, an ambient state data of a driving car are used. The ambient state data of a driving car considered in this study are that of the preceding car and the shape of the road. Experiments using a driving simulator are conducted to evalu

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Hu, Jie, and Sheng Luo. "A Car-Following Driver Model Capable of Retaining Naturalistic Driving Styles." Journal of Advanced Transportation 2020 (January 21, 2020): 1–16. http://dx.doi.org/10.1155/2020/6520861.

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The modeling of car-following behavior is an attractive research topic in traffic simulation and intelligent transportation. The driver plays an important role in car following but is ignored by most car-following models. This paper presents a novel car-following driver model, which can retain aspects of human driving styles. First, simulated car-following data are generated by using the speed control driver model and the real-world driving behavior data if the real-world car-following data are not available. Then, the car-following driver model is established by imitating human driving maneuv

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Hoc, Jean-Michel. "Car-driving assistance for safety." Le travail humain 69, no. 2 (2006): 97. http://dx.doi.org/10.3917/th.692.0097.

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Rane, Vedant, Hrithik Poojari, Prasan Sharma, Soham Phansekar, and Prof Prajakta Pawar. "LiDAR Based Self-Driving Car." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 261–70. http://dx.doi.org/10.22214/ijraset.2022.41213.

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Abstract: LiDAR, typically used as an acronym for “’light detection and ranging’”, is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real time. Its advantages include impressively accurate depth perception, which allows LiDAR to know the distance to an object to within a few centimetres, up to 60 metres away. It’s also highly suitable for 3D mapping, which means returning vehicles can then navigate the environment predictably —a significant benefit for most self-driving

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Tiemann, Kathleen A., Abdallah M. Badahdah, and Daphne E. Pedersen. "Driving a Car in Saudi." Teaching Sociology 37, no. 2 (2009): 188–93. http://dx.doi.org/10.1177/0092055x0903700206.

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Gavali, Priyanka, Punam Sontakke, Deepika Dhabhade, and A. P. Gargade. "Driving a Wireless Robotic Car." International Journal of Engineering Trends and Technology 47, no. 7 (2017): 375–79. http://dx.doi.org/10.14445/22315381/ijett-v47p262.

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Shinghal, Udit, Yashwanth A. V. Mowdhgalya, Vaibhav Tiwari, and Achyutha Prasad N. "Centaur - A Self-Driving Car." International Journal of Computer Trends and Technology 68, no. 4 (2020): 129–31. http://dx.doi.org/10.14445/22312803/ijctt-v68i4p121.

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Julie, I. Swann, and Kate Ryder. "Car adaptations to facilitate driving." British Journal of Healthcare Assistants 3, no. 6 (2009): 277–80. http://dx.doi.org/10.12968/bjha.2009.3.6.42788.

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Mukesh, D. "Mass Transfer and Car Driving." Journal of Chemical Education 72, no. 5 (1995): 436. http://dx.doi.org/10.1021/ed072p436.

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Clark, Barry A. J. "COLOURED LENSES AND CAR DRIVING." Acta Ophthalmologica 49, no. 5 (2009): 673–77. http://dx.doi.org/10.1111/j.1755-3768.1971.tb08662.x.

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Jackson, Hollie J., Sarwish Rafiq, and Renier J. Brentjens. "Driving CAR T-cells forward." Nature Reviews Clinical Oncology 13, no. 6 (2016): 370–83. http://dx.doi.org/10.1038/nrclinonc.2016.36.

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Baas, Tracey. "Driving CAR-based cellular therapies." Science-Business eXchange 6, no. 41 (2013): 1152. http://dx.doi.org/10.1038/scibx.2013.1152.

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Herzog, Roland W. "Driving the hemophilia tolerance CAR." Blood 129, no. 2 (2017): 142–44. http://dx.doi.org/10.1182/blood-2016-11-753160.

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Swales, Karen, and Masahiko Negishi. "CAR, Driving into the Future." Molecular Endocrinology 18, no. 7 (2004): 1589–98. http://dx.doi.org/10.1210/me.2003-0397.

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Patil, Shamal, Hrushikesh Bhujbal, Fatru Shaikh, Priya Lokhande, and Aashika Jain. "Self-Driving Car Using Simulator." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 2 (2024): 508–16. http://dx.doi.org/10.32628/ijsrset2411269.

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Due to rapid technological growth in transportation, self-driving cars became the topic of concern. The main purpose of this project is to use the CNN and train the neural network in order to drive the car in autonomous mode in a simulator environment. Front camera of a car captures the images and we use those captured images in order to train the model, in short we can say we have used the concept of behavioural cloning. In behavioural cloning, the system tries to mimic the human driving behaviour by tracking the steering angle. That means a dataset is generated in the simulator by a user dri

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Ingle, Suryangini. "IoT based Self Driving Car." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03630.

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Abstract This project shows an affordable self-driving car prototype that uses Raspberry Pi 4, motor driver, DC motors, USB webcam. The system combines software-control algorithms with hardware elements like DC motors, a motor driver (L298N), and a power supply. Using methods like grayscale conversion, Gaussian blur, etc. OpenCV processes a real-time video frame from the webcam to detect road boundaries, objects, signs, etc. The prototype uses either rule-based logic or a trained convolutional neural network (CNN) model to establish driving direction based on the data obtained from raspberry p

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Zhang, Siyang, Zherui Zhang, and Chi Zhao. "A Method of Intelligent Driving-Style Recognition Using Natural Driving Data." Applied Sciences 14, no. 22 (2024): 10601. http://dx.doi.org/10.3390/app142210601.

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At present, achieving efficient, sustainable, and safe transportation has led to increasing attention on driving behavior recognition and advancements in autonomous driving. Identifying diverse driving styles and corresponding types is crucial for providing targeted training and assistance to drivers, enhancing safety awareness, optimizing driving costs, and improving autonomous driving systems responses. However, current studies mainly focus on specific driving scenarios, such as free driving, car-following, and lane-changing, lacking a comprehensive and systematic framework to identify the d

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Devi, T. Kirthiga, Akshat Srivatsava, Kritesh Kumar Mudgal, Ranjnish Raj Jayanti, and T. Karthick. "Behaviour Cloning for Autonomous Driving." Webology 17, no. 2 (2020): 694–705. http://dx.doi.org/10.14704/web/v17i2/web17061.

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The objective of this project is to automate the process of driving a car. The result of this project will surely reduce the number of hazards happening everyday. Our world is in progress and self driving car is on its way to reach consumer‟s door-step but the big question still lies that will people accept such a car which is fully automated and driverless. The idea is to create an autonomous Vehicle that uses only some sensors (collision detectors, temperature detectors etc.) and camera module to travel between destinations with minimal/no human intervention. The car will be using a trained

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Liao, Lyuchao, Zewei Lin, Jinmei Lin, Dongmei Hu, Fumin Zou, and Shukun Lai. "Car-Following Model with Automatic Reaction Delay Estimation: An Attention-Based Ensemble Learning Methodology." Scientific Programming 2022 (January 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/5414559.

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Car-following behavior is a vital traffic phenomenon in the process of vehicle driving. For modeling the car-following behavior, it is crucial to capture the reaction delay for balancing with safety and comfort, but it is generally ignored in existing works. This work proposes a car-following model based on attention-based ensemble learning to automatically capture the reaction delay from driving data and better depict the traffic flow characteristics. The model integrates a data-driven model and a theory-driven model, and a weight computation method is proposed to combine the advantage of the

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Hwang, Kitae, In Hwan Jung, and Jae Moon Lee. "Implementation of Autonomous Driving on RC-CAR with Raspberry PI and AI Server." Webology 19, no. 1 (2022): 4444–58. http://dx.doi.org/10.14704/web/v19i1/web19293.

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A lot of research is being done on autonomous driving vehicles or robots that recognize objects and drive themselves without human intervention. In order to develop autonomous driving technology, there is a fundamental difficulty in securing expensive real cars equipped with various sensors. In this paper, an autonomous driving system development platform was developed using an inexpensive RC-Car, and a test system that can test various algorithms related to autonomous driving was introduced. In the system developed in this study, the single board computer Raspberry PI was mounted on the RC-Ca

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Prof, D. P. Radke, Bawankar Vaibhav, Deshmukh Sagar, Lambat Gauri, and Kharwade Bhargavi. "Artificial Intelligence Based Self-Driving Car." Advancement of Computer Technology and its Applications 3, no. 1 (2020): 1–9. https://doi.org/10.5281/zenodo.3698550.

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<em>In this modern era, the Automobile Industry is getting updated day by day with the implementation of the latest technologies in vehicles. Artificial Intelligence and Machine Learning are some of the best trending and useful technologies in the world. Google, the expert in technologies, working on such projects since 2010 and continuously making modifications to it to date. In this paper, we focus on the implementation of all these advanced technologies in the vehicle to make the vehicle automated. Vehicles can detect and analyze their surroundings with the help of OpenCV by which vehicles

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Hurair, Mohammad, Jaeil Ju, and Junghee Han. "Environmental-Driven Approach towards Level 5 Self-Driving." Sensors 24, no. 2 (2024): 485. http://dx.doi.org/10.3390/s24020485.

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As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address th

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Fan, Pengcheng, Jingqiu Guo, Haifeng Zhao, Jasper S. Wijnands, and Yibing Wang. "Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks." Sustainability 11, no. 23 (2019): 6755. http://dx.doi.org/10.3390/su11236755.

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Although a lot of work has been conducted on car-following modeling, model calibration and validation are still a great challenge, especially in the era of autonomous driving. Most challengingly, besides the immediate benefit incurred with a car-following action, a smart vehicle needs to learn to evaluate the long-term benefits and become foresighted in conducting car-following behaviors. Driving memory, which plays a significant role in car-following, has seldom been considered in current models. This paper focuses on the impact of driving memory on car-following behavior, particularly, histo

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Sviatov, Kirill, Nadejda Yarushkina, Daniil Kanin, et al. "Functional Model of a Self-Driving Car Control System." Technologies 9, no. 4 (2021): 100. http://dx.doi.org/10.3390/technologies9040100.

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The article describes a structural and functional model of a self-driving car control system, which generates a wide class of mathematical problems. Currently, control systems for self-driving cars are considered at several levels of abstraction and implementation: Mechanics, electronics, perception, scene recognition, control, security, integration of all subsystems into a solid system. Modern research often considers particular problems to be solved for each of the levels separately. In this paper, a parameterized model of the integration of individual components into a complex control syste

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Ma, Lijing, Shiru Qu, Lijun Song, Junxi Zhang, and Jie Ren. "Human-like car-following modeling based on online driving style recognition." Electronic Research Archive 31, no. 6 (2023): 3264–90. http://dx.doi.org/10.3934/era.2023165.

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<abstract><p>Incorporating human driving style into car-following modeling is critical for achieving higher levels of driving automation. By capturing the characteristics of human driving, it can lead to a more natural and seamless transition from human-driven to automated driving. A clustering approach is introduced that utilized principal component analysis (PCA) and k-means clustering algorithm to identify driving style types such as aggressive, moderate and conservative at the timestep level. Additionally, an online driving style recognition technique is developed based on the

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Wei, Bo, and Xing Yi Su. "Simulation and Calculation: Improve Work Life of Bearing on Driving Gear of Main Reducer." Applied Mechanics and Materials 644-650 (September 2014): 128–33. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.128.

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The driving gear and driven gear in car rear axle have function of transmitting power. As important part of supporting driving gear, work life and situation of bearing have influence on power transmission. This paper improves system stiffness of gear and bearing, improves force situation of bearing by researching span between two bearings on driving gear. Work situation of main reducer can be improved through extending bearing life. The vibration and noise are reduced. Car market competition is improved.

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R., Poonkuzhali, and Kumar Singh Vineet. "Virtual Self Driving Car using the Techniques of Image Processing and Deep Neural Networks." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 190–95. https://doi.org/10.35940/ijeat.D9070.069520.

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Self-driving cars come with both confronts and openings. Many tech gigantic companies like Google, Tesla, Apple and many more are funding billions of dollars for the implementation of a driverless car. In this modern era of automation, every human need has been driven towards things to be automated. From automated traffic control to automated home, everything comes up to the rescue of human to provide a comfortable and relaxing lifestyle. After almost automating everything now mankind has moved to automate the transportation, starting with automating the vehicles. With this the first step take

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Kuntohadi, Hendro, Yosi Pahala, and Rohana Sitanggang. "RISK MANAGEMENT ANALYSIS ON THE CAR DRIVERS IN INDONESIA." JURNAL MANAJEMEN TRANSPORTASI DAN LOGISTIK 2, no. 2 (2017): 221. http://dx.doi.org/10.25292/j.mtl.v2i2.125.

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Car drivers in Indonesia will always face many kinds of risks. This research contains the context determination of this research, identifies all the significant risks, measures the frequency and impact of all the risks, draws all the identified risks in the chart, and describes how to manage or mitigate the risks. The dangerous risks which have high frequency and high impact are: 1) Many car drivers get the driving license without taking a driving course and without learning carefully the theory of driving a car; 2) Many car drivers get the driving license through illegal procedure; 3) Many ca

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Kuntohadi, Hendro, Yosi Pahala, and Rohana Sitanggang. "RISK MANAGEMENT ANALYSIS ON THE CAR DRIVERS IN INDONESIA." Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) 2, no. 2 (2015): 221. http://dx.doi.org/10.54324/j.mtl.v2i2.125.

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Car drivers in Indonesia will always face many kinds of risks. This research contains the context determination of this research, identifies all the significant risks, measures the frequency and impact of all the risks, draws all the identified risks in the chart, and describes how to manage or mitigate the risks. The dangerous risks which have high frequency and high impact are: 1) Many car drivers get the driving license without taking a driving course and without learning carefully the theory of driving a car; 2) Many car drivers get the driving license through illegal procedure; 3) Many ca

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Korovai, K. O. "SELF-DRIVING CAR DILEMMAS. WHAT ETHICAL PROBLEMS CAN YOU FIND IN SELF-DRIVING CAR PROSPECTS?" UKRAINIAN CULTURAL STUDIES, no. 2 (7) (2020): 88–89. http://dx.doi.org/10.17721/ucs.2020.2(7).17.

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Zhang, Yanning, Zhongyin Guo, and Zhi Sun. "Driving Simulator Validity of Driving Behavior in Work Zones." Journal of Advanced Transportation 2020 (June 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/4629132.

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Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute

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Tang, Ai Hua, Jian Ping Tian, and Ying Hua Liao. "Analysis for Ride Comfort Evaluation of Passenger Car Traveling on Roads with Generalized Road Profiles and Conventional Speeds." Advanced Materials Research 926-930 (May 2014): 877–80. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.877.

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To investigate how the conventional speeds to affect passenger cars ride comfort under a kind of road surface profiles, in multibody dynamics software (ADAMS/Car), a vehicle model was built based on the characteristic parameters of a passenger car. According to the relevant test regulations of ride comfort, the building methods of road surface profiles were discussed. Furthermore, a dynamics simulation analysis of the car was realized by ADAMS/Car and the acceleration-time histories of the seat surfaces X/Y/Z-axis under three conventional driving-speeds were acquired. A special MATLAB program

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Poczter, Sharon L., and Luka M. Jankovic. "The Google Car: Driving Toward A Better Future?" Journal of Business Case Studies (JBCS) 10, no. 1 (2013): 7. http://dx.doi.org/10.19030/jbcs.v10i1.8324.

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Googles dramatic ascent and subsequent domination in the past fifteen years of the technology and information industries has financially enabled Google to explore seemingly unrelated projects ranging from Google Mail to the Google Car. In particular, Google has invested a significant amount of resources in the Google Car, an integrated system that allows for the driverless operation of a vehicle. While initial reports indicate that the Google Car driverless automobile will be more safe and efficient than current vehicles, the Google Car is not without its critics. In particular, the existentia

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Lee, Joey, Benedikt Groß, and Raphael Reimann. "Who wants to be a self-driving car?" Information Design Journal 25, no. 1 (2019): 21–27. http://dx.doi.org/10.1075/idj.25.1.02lee.

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Abstract Self-driving cars and autonomous transportation systems are projected to create radical societal changes, yet public understanding and trust of self-driving cars and autonomous systems is limited. The authors present a new mixed-reality experience designed to provide its users with insights into the ways that self-driving cars operate. A single-person vehicle equipped with sensors provides its users with data driven visual feedback in a virtual reality headset to navigate in physical space. The authors explore how immersive experiences might provide ‘conceptual affordances’ that lower

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Shen, Chih-Hsiung, and Ting-Jui Hsu. "Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks." Applied Sciences 11, no. 1 (2020): 7. http://dx.doi.org/10.3390/app11010007.

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When driving on roads, the most important issue for driving is safety. There are various vehicles, including cars, motorcycles, bicycles, and pedestrians, that increase the complexity of road conditions and the burden on drivers. In order to improve driving safety, a deep learning framework is applied to predict and announce the trajectory of a car. This research is divided into three parts. Lane line detection is adopted first. Secondly, car object detection is employed. Lastly, car trajectory prediction is a key part of our research. In addition, real images and videos in the driving recorde

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den, Braver Nicolette R., Julia G. Kok, Joreintje D. Mackenbach, et al. "Neighbourhood drivability: environmental and individual characteristics associated with car use across Europe." International Journal of Behavioral Nutrition and Physical Activity 17, no. 1 (2020): 8. https://doi.org/10.1186/s12966-019-0906-2.

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<strong>Background: </strong>Car driving is a form of passive transportation associated with higher sedentary behaviour, which is associated with morbidity. The decision to drive a car is likely to be influenced by the 'drivability' of the built environment, but there is lack of scientific evidence regarding the relative contribution of environmental characteristics of car driving in Europe, compared to individual characteristics. This study aimed to determine which neighbourhood- and individual-level characteristics were associated with car driving in adults of five urban areas across Europe.

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