Статті в журналах: "Car driving"

1

T., Dr Manikandan. "Self Driving Car." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 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 (February 21, 2017): 88. http://dx.doi.org/10.1145/3047404.

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Dubey, Ashutosh, and Prabakaran N. "SELF-DRIVING CAR SIMULATION." International Research Journal of Computer Science 07, no. 05 (May 25, 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 (October 31, 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 developed a technology for cars that drives it automatically using LIDAR. This car is capable of sensing the surroundings, navigating and fulfilling the human transportation capabilities without any human input. It continuously tracks the surrounding and if any obstacle is detected vehicle senses and moves around and avoids the obstacle. An autonomous car navigation system based on Global Positioning System (GPS) is a new and promising technology, which uses real time geographical data received from several GPS satellites to calculate longitude, latitude, speed and course to help navigate a car. As we know the development of gps is more improved now the accuracy of gps we can see centimetre also so Like for our car to go at specific inputted location we use this gps technology.Lidar is used for sensing the surroundings. Like radar, lidar is an active remote sensing technology but instead of using radio or microwaves it uses electromagnetic waves. Keywords: Congestion, Traffic Accident, LIDAR sensor, Global Positioning System, Electromagnetic waves

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5

M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (August 31, 2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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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 (February 28, 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 (June 1, 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. Their emotional responses to cars and driving, shaped by transport norms and infrastructures in their places of origin, ranged from pragmatism and ambivalence to fear and hostility. The lived experiences of these migrants show that multiple cultures of mobility coexist, even in ostensibly car-dependent societies. Migrants’ life histories and contemporary practices provide an opportunity to reflect on fissures in the logic of automobility.

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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 (January 2, 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-driving car is predicted. Findings This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car. Originality/value The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

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9

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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10

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 (April 30, 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 technologies. One of the key strengths of LiDAR is the number of areas that show potential for improvement. These include solid-state sensors, which could reduce its cost tenfold, sensor range increases of up to 200m, and 4-dimensional LiDAR, which senses the velocity of an object as well as its position in 3-D space. However, despite these exciting advances, LiDAR is still hindered by a key factor; its significant cost. LiDAR is not the only self-driving detection technology, with cameras as the major rival, championed by Tesla as the best way forward. Elon Musk has described LiDAR as “a fool’s errand” and “unnecessary”. The argument runs that humans drive based only on ambient visible light, so robots should equally be able to. A camera is significantly smaller and cheaper than LiDAR (although more of them are needed), and has the advantage of seeing in better resolution and in colour, meaning it can read traffic lights and signs. However, cameras have a wide host of characteristics that make them tricky to use in common driving conditions. Whereas LiDAR uses near infra-red light, cameras use visible light, and are thus more susceptible to issues when faced with rain, fog, or even some textures. In addition, LiDARs do not depend on ambient light, generating their own light pulses, whereas cameras are more sensitive to sudden light changes, direct sunlight and even raindrops. Keywords: Congestion, Traffic Accident, LIDAR sensor, Global Positioning System, Electromagnetic waves

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11

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

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12

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 (May 25, 2017): 375–79. http://dx.doi.org/10.14445/22315381/ijett-v47p262.

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13

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 (April 25, 2020): 129–31. http://dx.doi.org/10.14445/22312803/ijctt-v68i4p121.

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14

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

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15

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

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16

Clark, Barry A. J. "COLOURED LENSES AND CAR DRIVING." Acta Ophthalmologica 49, no. 5 (May 27, 2009): 673–77. http://dx.doi.org/10.1111/j.1755-3768.1971.tb08662.x.

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17

Jackson, Hollie J., Sarwish Rafiq, and Renier J. Brentjens. "Driving CAR T-cells forward." Nature Reviews Clinical Oncology 13, no. 6 (March 22, 2016): 370–83. http://dx.doi.org/10.1038/nrclinonc.2016.36.

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18

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

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19

Herzog, Roland W. "Driving the hemophilia tolerance CAR." Blood 129, no. 2 (January 12, 2017): 142–44. http://dx.doi.org/10.1182/blood-2016-11-753160.

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20

Swales, Karen, and Masahiko Negishi. "CAR, Driving into the Future." Molecular Endocrinology 18, no. 7 (July 1, 2004): 1589–98. http://dx.doi.org/10.1210/me.2003-0397.

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21

Devi, T. Kirthiga, Akshat Srivatsava, Kritesh Kumar Mudgal, Ranjnish Raj Jayanti, and T. Karthick. "Behaviour Cloning for Autonomous Driving." Webology 17, no. 2 (December 21, 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 Convolutional Neural Network (CNN) which would control the parameters that are required for smoothly driving a car. They are directly connected to the main steering mechanism and the output of the deep learning model will control the steering angle of the vehicle. Many algorithms like Lane Detection, Object Detection are used in tandem to provide the necessary functionalities in the car.

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22

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 (January 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 evaluate the effectiveness of considering the ambient state of a driving car.

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23

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 (January 20, 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-Car to control the car, and the autonomous driving-related algorithms were implemented in a separate AI server, and they communicated with the message-based ROS protocol. In addition, those who want to develop an autonomous driving system can easily attach desired sensors to the RC-Car, increasing scalability. In this paper, almost all algorithms related to autonomous driving have been implemented. A simple autonomous driving RC-Car system was actually implemented and operation was verified by designing and implementing algorithms such as lane recognition, driving along the lane, obstacle detection and stopping, traffic light recognition, driving between smooth and sharp curves, and autonomous parking. In sharp curves, the angle of the lane was tracked in a short period to prevent the vehicle from crossing the lane. In addition, we developed an Android app that can manually control the car and monitor the video from the camera in time. This study presented and solved various difficulties that could not be known by developing an autonomous driving algorithm using simulators.

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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 maneuver during real-world car following. This is accomplished by using a neural network-based learning control paradigm and car-following data. Finally, the FTP-72 driving cycle is borrowed as the speed profile of the leading vehicle for the model test. The driving style is quantitatively analyzed by AESD. The results show that the proposed car-following driver model is capable of retaining the naturalistic driving styles while well accomplishing the car-following task with the error of relative distance mostly less than 5 meters for every driving styles.

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25

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 these two different models. In detail, an encoder-decoder model and attention mechanism are employed to capture the reaction delay from driving data. Extensive experiments show that the proposed model could balance safety with comfort and help avoid unsafe driving behavior.

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Sviatov, Kirill, Nadejda Yarushkina, Daniil Kanin, Ivan Rubtcov, Roman Jitkov, Vladislav Mikhailov, and Pavel Kanin. "Functional Model of a Self-Driving Car Control System." Technologies 9, no. 4 (December 10, 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 system for a self-driving car is considered. Such a model simplifies the design and development of self-driving control systems with configurable automation tools, taking into account the specifics of the solving problem. The parameterized model can be used for CAD design in the field of self-driving car development. A full cycle of development of a control system for a self-driving truck was implemented, which was rub in the “Robocross 2021” competition. The software solution was tested on more than 40 launches of a self-driving truck. Parameterization made it possible to speed up the development of the control system, expressed in man-hours, by 1.5 times compared to the experience of the authors of the article who participated in the same competition in 2018 and 2019. The proposed parameterization was used in the development of individual CAD elements described in this article. Additionally, the implementation of specific modules and functions is a field for experimental research.

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27

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 and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.

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28

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 (November 28, 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, historical driving memory represents certain types of driving regimes and drivers’ maneuver in coordination with the variety of driving regimes. An autoencoder was used to extract the main features underlying the time-series data in historical driving memory. Long short-term memory (LSTM) neural network model has been employed to investigate the relationship between driving memory and car-following behavior. The results show that velocity, relative velocity, instant perception time (IPT), and time gap are the most relevant parameters, while distance gap is insignificant. Furthermore, we compared the accuracy and robustness of three patterns including various driving memory information and span levels. This study contributes to bridging the gap between historical driving memory and car-following behavior modeling. The developed LSTM methodology has the potential to provide personalized warnings of dangerous car-following distance over the next second.

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29

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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30

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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31

Kuntohadi, Hendro, Yosi Pahala, and Rohana Sitanggang. "RISK MANAGEMENT ANALYSIS ON THE CAR DRIVERS IN INDONESIA." JURNAL MANAJEMEN TRANSPORTASI DAN LOGISTIK 2, no. 2 (July 25, 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 car manufacturers eliminate some car safety equipment to lower the sales price; 4) The ingredients of the gasoline cannot fulfill/match the need of the car (e.g. RON number); 5) In some places the traffic is too crowded; 6) Many damaged roads may cause accidents; 7) Many roads and transportation modes cannot fulfill the demands of transportation. The recommendation (mitigation) for Risk Controlling is that all stakeholders (legislative, executive, judicative, manufacturers, people, car drivers, schools and universities, researchers, etc) should obey the regulations, moral, ethics for car riders. It is mandatory that every candidate of car driver take a course in a certified car driving ourse. Police should arrange a complete and comprehensive reference book for car driver candidates to get a driving license.

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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 (July 7, 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 car manufacturers eliminate some car safety equipment to lower the sales price; 4) The ingredients of the gasoline cannot fulfill/match the need of the car (e.g. RON number); 5) In some places the traffic is too crowded; 6) Many damaged roads may cause accidents; 7) Many roads and transportation modes cannot fulfill the demands of transportation. The recommendation (mitigation) for Risk Controlling is that all stakeholders (legislative, executive, judicative, manufacturers, people, car drivers, schools and universities, researchers, etc) should obey the regulations, moral, ethics for car riders. It is mandatory that every candidate of car driver take a course in a certified car driving ourse. Police should arrange a complete and comprehensive reference book for car driver candidates to get a driving license.

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33

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 was compiled to calculate the total weighted Root Mean Square (RMS) value by calling the above histories. According to the GB/T 4970-1996, a road test of a passenger car was carried out in the random road surface which equivalent to B level. The car was driven to get the values of total weighted acceleration RMS under three conventional driving-speeds. By comparing the road test result with simulation, the result indicated that the changing trend of total weighted RMS value is consistent as the driving-speed changes, and the ride comfort will decrease when the driving-speed increase. At the same time, it shows that the consistency of the simulation and road test is better.

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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 (December 31, 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 existential threat that the car presents to several large industries, including the insurance, health care and construction industries, creates an additional challenge to the success of the Google Car well beyond the standard competitive threats from other established car manufacturers in the automobile industry, which begs the question, Can the Google Car be successful? With so many challenges above and beyond the competitive forces typically threatening long-term profitability, will the Google Car be able to create and sustain a competitive advantage for Google in the driverless car space?

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35

Lee, Joey, Benedikt Groß, and Raphael Reimann. "Who wants to be a self-driving car?" Information Design Journal 25, no. 1 (December 31, 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 the entry barrier for diverse audiences to discuss complex topics.

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Ziyan, Chen, and Liu Shiguo. "China's self-driving car legislation study." Computer Law & Security Review 41 (July 2021): 105555. http://dx.doi.org/10.1016/j.clsr.2021.105555.

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HAGIWARA, Ichiro, Luis DIAGO, and Hiroe ABE. "Mathematical Sciences for Self-Driving Car." Proceedings of Mechanical Engineering Congress, Japan 2021 (2021): W011–01. http://dx.doi.org/10.1299/jsmemecj.2021.w011-01.

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Ahmed, Meraj. "Autonomous driving car using machine learning." Global Sci-Tech 11, no. 3 (2019): 170. http://dx.doi.org/10.5958/2455-7110.2019.00025.9.

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Cerf, Vinton G. "A comprehensive self-driving car test." Communications of the ACM 61, no. 2 (January 23, 2018): 7. http://dx.doi.org/10.1145/3177753.

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Jonsson, Bertil, Hans Stenlund, and Ulf Björnstig. "Backset—Stationary and During Car Driving." Traffic Injury Prevention 9, no. 6 (December 9, 2008): 568–73. http://dx.doi.org/10.1080/15389580802308312.

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Marigo, Alessia, and Benedetto Piccoli. "SAFETY DRIVING OF THE DUBINS' CAR." IFAC Proceedings Volumes 35, no. 1 (2002): 161–66. http://dx.doi.org/10.3182/20020721-6-es-1901.01580.

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42

Schultz, Liora, and Crystal Mackall. "Driving CAR T cell translation forward." Science Translational Medicine 11, no. 481 (February 27, 2019): eaaw2127. http://dx.doi.org/10.1126/scitranslmed.aaw2127.

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Warnes, Anthony M., and David A. Fraser. "Car Driving as a Social Skill." Gerontology & Geriatrics Education 13, no. 1-2 (March 24, 1993): 103–27. http://dx.doi.org/10.1300/j021v13n01_08.

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Wewerinke, P. H. "Modeling Car Driving and Road Traffic." IFAC Proceedings Volumes 28, no. 15 (June 1995): 511–16. http://dx.doi.org/10.1016/s1474-6670(17)45283-9.

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45

Shen, Chih-Hsiung, and Ting-Jui Hsu. "Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks." Applied Sciences 11, no. 1 (December 22, 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 recorder are used to simulate the real situation the driver sees from the driver’s seat. Car detection is utilized to obtain the coordinates of the car in these images, reaching an accuracy of 0.91 and then predicting the future trajectory of the car, obtaining a loss of 0.00024 and costing 12 milliseconds. It can precisely mark the position of the car, accurately detect the lane line, and predict the future car’s trajectory. Through the prediction and announcement of the car trajectory, we verified that our model can correctly predict the car trajectory and truly enhance the safety of driving.

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46

C V, Rajath. "Self-Driving Car using Deep-Q-Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3621–25. http://dx.doi.org/10.22214/ijraset.2021.35753.

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A autonomous car is also called a self-driving car or a robot car. As for the history of self-driving cars, radio technology was used to control the tests, which began in 1920, and later in 1950, the tracks were finally put in place. The present-day individual is habituated to automation technology and the use of robotics in areas such as agriculture, medication, transportation, IT industry, etc. In the recent decades, the automotive sector has come to the forefront of researching private car technologies. The independent Level-3 standard was out in 2020. Everyday automotive technology researchers solve challenges. The prime intention of the project is to create a self-driving car using Deep-Q-Networks, thus enabling the car to make decisions based on the spontaneously occurring events. Independent vehicles require data and are regularly updated, therefore IoT and AI can assist in allocating device data to the machine.

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Sabapathypillai, Sharon, Monica Perlmutter, Manik Goel, Bradley Wilsone Gordon, and Anjali Bhorade. "20201 Validating an in-car telemetry system for detecting frequency and severity of driving errors in patients with glaucoma." Journal of Clinical and Translational Science 5, s1 (March 2021): 29. http://dx.doi.org/10.1017/cts.2021.479.

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ABSTRACT IMPACT: The car telemetry system may be an ideal method to accurately and reliably evaluate and compare at-risk driving errors between older drivers with and without glaucoma. OBJECTIVES/GOALS: Our project aims to determine whether an in-car telemetry system used during an on-road driving evaluation can accurately and reliably evaluate driving errors in lane maintenance and visual scanning and objectively quantify the frequency and severity of these errors in glaucoma patients. METHODS/STUDY POPULATION: This is a single center, cross-sectional study of 180 participants (125 with glaucoma and 55 controls), ages 55 or older, who underwent a comprehensive clinical assessment, including vision, cognition, motor function, followed by an on-road evaluation by a trained occupational therapist. Driving errors were recorded through a dual method including: 1. An in-car trained occupational therapist 2. In-car telemetry system. The frequency and severity of errors in lane maintenance and visual scanning from the in-car telemetry will be assessed and compared between participants with varying severity of glaucoma and normal controls. In addition, we will compare the frequency and severity of errors in lane maintenance and visual scanning to those recorded by the in-car driving evaluator. RESULTS/ANTICIPATED RESULTS: We anticipate (or predict) that the in- car telemetry system will be able to detect frequency and severity of driving errors in lane maintenance and visual scanning in glaucoma participants. We also predict that participants with worsening glaucoma severity will commit more driving errors. In addition, the in-car telemetry will detect a similar frequency and severity of driving errors as the in-car driving evaluator. DISCUSSION/SIGNIFICANCE OF FINDINGS: The type and frequency of vision-related driving errors that place individuals at risk for a car accident is not well known. Without this critical information, it is extremely challenging to help older adults with glaucoma to be safe drivers.

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Barahona, Nano, Francisco A. Gallego, and Juan-Pablo Montero. "Vintage-Specific Driving Restrictions." Review of Economic Studies 87, no. 4 (May 24, 2019): 1646–82. http://dx.doi.org/10.1093/restud/rdz031.

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Abstract Local air pollution has led authorities in many cities around the world to impose limits on car use by means of driving restrictions or license-plate bans. By placing uniform restrictions on all cars, many of these programs have created incentives for drivers to buy additional, more polluting cars. We study vintage-specific restrictions, which place heavy limits on older, polluting vehicles and no limits on newer, cleaner ones. We use a novel model of the car market and results from Santiago’s 1992 program, the earliest program to use vintage-specific restrictions, to show that such restrictions should be designed to work exclusively through the extensive margin (type of car driven), never through the intensive margin (number of miles driven). If so, vintage restrictions can yield important welfare gains by moving the fleet composition toward cleaner cars, comparing well to alternative instruments such as scrappage subsidies and pollution-based registration fees.

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Xie, Yizhen, Qichao Ni, Osama Alfarraj, Haoran Gao, Guojiang Shen, Xiangjie Kong, and Amr Tolba. "DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data." Wireless Communications and Mobile Computing 2020 (December 1, 2020): 1–9. http://dx.doi.org/10.1155/2020/8816681.

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The car-following model describes the microscopic behavior of the vehicle. However, the existing car-following models set the drivers’ reaction time to a fixed value without considering its dynamics. In order to improve the accuracy of car-following model, this paper proposes Deep Feature Learning-based Car-Following Model (DeepCF), a car-following model based on fatigue driving and Generative Adversarial Networks (GAN). The model is composed of the drivers’ reaction time model and the car-following decision algorithm. First, we regard driving fatigue as the starting point to study the influence of driving time and the acceleration of the preceding vehicle on the drivers’ reaction time, and develop a coarse-grained drivers’ reaction time model. Secondly, considering the impact of fatigue driving on car-following decisions, we utilize GAN to generate a driving decision database based on reaction time and use Euclidean distance as a decision search indicator. Finally, we conduct experiments on a real data set, and the results indicate that our DeepCF model is superior to baseline models.

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Sandry, Eleanor. "Automation and human relations with the private vehicle: from automobiles to autonomous cars." Media International Australia 166, no. 1 (November 1, 2017): 11–19. http://dx.doi.org/10.1177/1329878x17737644.

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This article theorises interactions between drivers, cars and their surroundings. It uses Don Ihde’s four human–technology relations – embodiment, hermeneutic, alterity and background – to analyse the ways in which human–car relationships develop through the process of driving. When driving non-autonomous cars, humans are engaged with cars in driver–car assemblages, within which they are aware of not only the car and how they control its movement but also the road and environmental conditions conveyed through the car. In autonomous and semi-autonomous cars, the connection between driver and car, process of driving and surrounding environment is disrupted. This may be regarded as a positive change, freeing drivers from the work of driving to enjoy rest and entertainment as they become passengers; however, as the car moves into a background relation with people inside the vehicle, it can become difficult for a human to take back control of the driving process with little warning.

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