Log in

Relevant bibliographies by topics / Driving behaviors

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Driving behaviors'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Driving behaviors.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Driving behaviors"

1

Rao, Dr B. Srinivasa, U. Sri Devi, and K. Sri Satya Harsha A. Rakesh K. Muralidhar. "Abnormal Driving Behaviors detection with smart phones." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1384–87. http://dx.doi.org/10.31142/ijtsrd11339.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Yang, Xiao Yu, Dan Li, and Peng Jun Zheng. "Effects of Eco-Driving on Driving Performance." Applied Mechanics and Materials 178-181 (May 2012): 2859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.2859.

Full text

Abstract:

This paper investigates the effects of two driving behaviors on driving performance, the driving with eco-driving support and the general driving. Through observing and analyzing these driving behaviors in a variety of situations, driving performance under conditions of with and without eco-driving was evaluated. Based on the measurements on fuel consumption, speed control and gear use, it was found that eco-driving device can guide drivers to take proper driving behavior, such as in which way to drive and how to drive in order to achieve energy saving. The paper revealed the effects of eco-driving and how to drive efficiently.

APA, Harvard, Vancouver, ISO, and other styles

3

Nachmann, Karl, Benjamin Pillot, Petrina Moore, and Eva Wiese. "Driving with Robots: Mind perception and propensity for aggressive driving." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 1965–70. http://dx.doi.org/10.1177/1071181320641473.

Full text

Abstract:

Mind perception, or the tendency to ascribe agency (i.e., the ability to plan and act) and experience (i.e., the ability to sense and feel) to others, is an important design consideration for human-robot inter-action since an agent’s mind status affects how we interact with it and how we interpret its behavior. The current study examines whether observable behaviors of robot-piloted autonomous vehicles are interpreted differently, lead to different emotional reactions and trigger different behaviors of the ob-server as a function of the robot driver’s perceived mind status. We expect that aggressive behavior of robot drivers perceived to be high in agency would be interpreted as more intentional, and as such would lead to stronger negative reactions and retaliatory behaviors. Consistent with our expectations, the robot driver high in agency was perceived as more intentional and elicited more irritation in partici-pants.

APA, Harvard, Vancouver, ISO, and other styles

4

Zou, Xi, and David M. Levinson. "Modeling Pipeline Driving Behaviors." Transportation Research Record: Journal of the Transportation Research Board 1980, no. 1 (2006): 16–23. http://dx.doi.org/10.1177/0361198106198000104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

Zang, Jinrui, Guohua Song, Yizheng Wu, and Lei Yu. "Method for Evaluating Eco-Driving Behaviors Based on Vehicle Specific Power Distributions." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (2019): 409–19. http://dx.doi.org/10.1177/0361198119853561.

Full text

Abstract:

Eco-driving is an effective way to reduce vehicle fuel consumption and exhaust emissions. Numerous studies have been conducted on eco-driving, however, there is still a lack of quick and accurate methods for evaluating eco-driving behaviors. This paper proposes a novel method to evaluate eco-driving behaviors based on vehicle specific power (VSP) distributions. First, the baseline speed-specific VSP distributions were derived based on second-by-second vehicle activity data of driving trajectories from 159 drivers on expressways in Beijing. Then, individual drivers’ speed-specific VSP distributions were developed for comparison with the baseline VSP distributions. A model was proposed to evaluate eco-driving behaviors based on the identified differences. Additionally, an eco-driving index (EDI) was designed to quantify the ecological level of driving behaviors for different speed ranges. The consistency of individual driving behaviors across different speed ranges was assessed. The minimum sample size and the appropriate speed bins required for reliable evaluation of individual eco-driving were also determined. The results showed that the differences between individual drivers’ VSP distributions and the baseline distributions could be used to identify eco-driving behaviors, and the eco-driving behaviors of individual drivers were consistent for different speed ranges. The minimum sample size for a reliable evaluation of individual eco-driving behaviors is 420 seconds. Data for speed bins above 70 km/h and below 10 km/h were not representative of the driving behavior and the driving behavior was especially consistent in the speed bins from 20 km/h to 40 km/h.

APA, Harvard, Vancouver, ISO, and other styles

7

Sweeney, Margaret M., and Carol Jarboe. "The Relationship between Driving Knowledge and Driving Behaviors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 24 (1996): 1284. http://dx.doi.org/10.1177/154193129604002466.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Ma, Changxi, Wei Hao, Wang Xiang, and Wei Yan. "The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents." Journal of Advanced Transportation 2018 (October 22, 2018): 1–10. http://dx.doi.org/10.1155/2018/9841498.

Full text

Abstract:

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

APA, Harvard, Vancouver, ISO, and other styles

9

Ma, Chunmei, Xili Dai, Jinqi Zhu, Nianbo Liu, Huazhi Sun, and Ming Liu. "DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration." Mobile Information Systems 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/9075653.

Full text

Abstract:

Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.

APA, Harvard, Vancouver, ISO, and other styles

10

Zhang, Jun, ZhongCheng Wu, Fang Li, et al. "A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data." Sensors 19, no. 6 (2019): 1356. http://dx.doi.org/10.3390/s19061356.

Full text

Abstract:

Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Driving behaviors"

1

Malta, Lucas, Akira Ozaki, Chiyomi Miyajima, Norihide Kitaoka, and Kazuya Takeda. "A multimedia corpus of driving behaviors." IEEE, 2009. http://hdl.handle.net/2237/13904.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Fox, Russell Thomas. "Examining Attention, Impulsiveness, and Cognitive Failures in Driving Behaviors." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etd/1465.

Full text

Abstract:

Dangerous driving behaviors are influenced by multiple factors including cognitive processes such as impulse inhibition and attentiveness. Impulsiveness, inattention, and cognitive failures have been linked to other risky behaviors, but a comprehensive evaluation using multiple methods of measurement of these has never been conducted to analyze their impact on dangerous driving. The purpose of this study was to examine influences of attentional abilities, impulsiveness, and cognitive failures on reported and demonstrated dangerous driving behaviors. Seventy-five participants completed a self-report dangerous driving measure, a self-report ADHD measure, a self-report impulsiveness measure, a continuous performance task to measure behavioral impulsivity and inattention, a measure of cognitive failures, and a driving simulator task. Two hierarchical linear regressions with simultaneous entry into blocks were used to analyze contributions of impulsiveness, inattention, and cognitive failures assessments in predicting dangerous driving behavior. Results indicated these assessments accounted for a significant proportion of the variance in Dula Dangerous Driving Index (3DI) scores above and beyond the effects of age and sex, Adjusted R▓ = .20, F(6, 59) = 2.51, p < .05, but no significant individual predictors emerged. Scores on these measures were also found to account for a significant amount of the variance in risky driving as measured by the driving simulator, above and beyond the effects of age and sex, Adjusted R▓ = .15, F(6, 60) = 2.91, p < .05, and identified BIS-11 scores and ADHD-RS impulsiveness scores as significant individual predictors. It seems that despite multiple methods of assessment, it is still difficult to capture the assumed relationships between each of these factors and driving. Though each assessment measures different aspects of constructs related to dangerous driving, the lack of relationships and predictive abilities may indicate that impulsiveness, inattention, cognitive failures, and dangerous driving may be more complex and multifaceted than previously understood.

APA, Harvard, Vancouver, ISO, and other styles

3

Lea, Erin J. "Selection, Optimization, and Compensation in the Self-Regulatory Driving Behaviors of Older Adults." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1259949239.

Full text

Abstract:

Thesis(M.A.)--Case Western Reserve University, 2010<br>Title from PDF (viewed on 2010-01-28) Department of Psychology Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

APA, Harvard, Vancouver, ISO, and other styles

4

Arbie, Nurlayla. "Exploratory Study of Distracted Behaviors of Transit Operators." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50433.

Full text

Abstract:

Bus transit driving is an occupation that requires high concentration in driving and is demanding due to work overload, time pressure, and responsibility for lives. In 2006, there were 103 fatal crashes involving transit buses. As the number of distraction-related crashes increases, it is important to conduct a transit distraction study to reduce future crashes. This thesis focused on the analysis of the likelihood of the operator distraction behaviors and the analysis to find a predictive model to classify different distraction categories. An ordinal logistic regression was carried out to evaluate how age, gender, driving experience of the operators, and their driving frequencies accounts for the likelihood of 17 potential distracted driving behaviors. The results of this analysis showed that there were only 5 best models (p-value of model fit less than 0.005 and p-value of parallel line test more than 0.005) that could be constructed, including: listening to the radio/ CD/DVD/MP3 player (D1); picking Up and Holding 2-way Radio (D5); listening to the Dispatch Office broadcast (D6); adjusting switches/controls on dashboard (D15); and utilizing mentor ranger (D16). On the other hand, a discriminant analysis was performed to predict how different transit operator driving behaviors when exposed by 10 different distraction activities and 16 predictors were considered in this analysis. The final results showed that there are 4 predictors that seem to be able to classify distraction groups across all 4 models; those include segment length, average duration of idling time/stop delay at speed interval 0—4 km/hr, frequency of speed transitions that deviate by ± 0 to 4 km/hr from its speed, and frequency of speed transitions that deviate by ± 8 to 12 km/hr from its speed.<br>Master of Science

APA, Harvard, Vancouver, ISO, and other styles

5

Shepherd, Betty Turner. "An investigation of judicial behaviors regarding the driving and drinking problem." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/49988.

Full text

Abstract:

The problem of driving and drinking has been examined in terms of prevention, enforcement, punishment, and education. From the sale of alcoholic beverages, it remains apparent that people will continue to drink and problems associated with that behavior will persist. The purpose of this study was to investigate how the judges in Montgomery County, Virginia, treated defendants brought to court for driving while under the influence of alcohol or driving on a license suspended due to alcohol abuse from July, 1982 through September, 1983. An analysis of the role played by the Montgomery County, Virginia, judges in the driving and drinking problem has shown that there were significant differences in the (number of continuations allowed, the type of verdict granted, and the form of punishment given. Defendants arrested for driving while under the influence of alcohol were much more likely to receive a guilty verdict (81%) than were people arrested for driving on a license suspended due to alcohol abuse (34%). These same judges were consistent in their treatment of male and female defendants in all areas except punishment where it was found that no females went to jail. Personal interviews with the judges substantiated the statistical results, but of even more significance was the accent placed on educating both the public, beginning in elementary school, and the drunk driver. Many recommendations for further research and further action were presented.<br>Ed. D.<br>incomplete_metadata

APA, Harvard, Vancouver, ISO, and other styles

6

Bumgarner, David J. "Anger Rumination, Stress, and Dangerous Driving Behaviors as Mediators of the Relationship between Multiple Dimensions of Forgiveness and Adverse Driving Outcomes." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2559.

Full text

Abstract:

Motor-Vehicle crashes are the leading cause of death for teens and young adults. Research and public interventions have primarily examined the impact of external factors related to driving; however, less work has examined internal factors. Limited research has shown a negative association between trait forgiveness of others and both driving anger and driving aggression. The current study replicates previous findings and expands to include multiple dimensions of forgiveness and adverse driving outcomes as a dependent variable. It was predicted that multiple dimensions of forgiveness would be directly and indirectly related to adverse driving outcomes through the mediators of anger rumination, stress, and dangerous driving. Undergraduate students (N=759) at a regional university completed a series of self-report questionnaires online examining driving anger, driving aggression, multiple dimensions of forgiveness, adverse driving outcomes, anger rumination, stress, and dangerous driving behaviors. Hierarchical multiple regression analyses were used to replicate previous findings (analysis 1) and multiple serial mediations as expansion (analysis 2). In replication, trait forgiveness of others was shown to have a negative bivariate correlation with driving anger and driving aggression and to be a significant predictor of driving aggression above that of driving anger (analysis 1). Multiple serial mediation demonstrated an indirect only effect of multiple dimensions of forgiveness on adverse driving outcomes through the various mediators (analysis 2); however, varied relationships were observed. As a result, forgiveness of self and of uncontrollable situations demonstrated a significant negative effect on adverse driving outcomes through the various mediators. However, although, forgiveness of others was found to have a significant negative effect through anger rumination and dangerous driving behaviors in serial, it demonstrated a positive effect with stress as a mediator. The results support and replicate previous research and demonstrate a significant indirect only effect of multiple dimensions of forgiveness on adverse driving outcomes through the current mediators. The relationships were varied, however. Therefore, multiple dimensions of forgiveness continue to be meaningful variables related to driving anger, driving aggression, and adverse driving outcomes.

APA, Harvard, Vancouver, ISO, and other styles

7

Glickman, Nathalia S. "Cell behaviors driving convergence and extension of the dorsal mesoderm of zebrafish /." view abstract or download file of text, 2000. http://wwwlib.umi.com/cr/uoregon/fullcit?p9998031.

Full text

Abstract:

Thesis (Ph. D.)--University of Oregon, 2000.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 106-112). Also available for download via the World Wide Web; free to University of Oregon users.

APA, Harvard, Vancouver, ISO, and other styles

8

Blowers, Andrew Pierce. "Stimulus Equivalence and the Emergence of Topography Based Driving Behaviors on a Vehicle Simulator Task." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/theses/1456.

Full text

Abstract:

This investigation assessed the utility of a selection-based instructional program in teaching relations between driving behavior and driving stimuli in addition to the emergence of topography-based responding. A selection-based instructional program was delivered to three individuals with intellectual disabilities and/or learning disabilities in order to teach participants relations of sameness between automobile operation stimuli and driving behaviors. Participants were directly taught relations between video models of vehicle operation, road sign outlines, and textual stimuli of road signs using a selection-based instructional protocol delivered via a computer program. Following mastery of the selection-based instruction the emergence of selection-based responding on symmetrical and transitive posttest probes at the mastery level was observed for all 3 participants. Furthermore, movement on posttest generalization vehicle simulator probe was observed for one participant.

APA, Harvard, Vancouver, ISO, and other styles

9

McLernon, Michelle Yvonne. "Risk Propensity, Self-Efficacy and Driving Behaviors Among Rural, Off-Duty Emergency Services Personnel." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/837.

Full text

Abstract:

Emergency medical services personnel work in a fast-paced, stressful environment requiring rapid, efficient response to critical situations, creating unique safety considerations within the workforce. With an occupational fatality rate notably higher than average, most of which are attributed to vehicular crashes, compounded by risks faced on rural roadways, rural EMS personnel face unique driving challenges that may be exacerbated by the very traits, self-efficacy and risk propensity, that may have initially drawn them to the profession. The purpose of this study was to identify the extent to which rural EMS personnel engage in off-duty, risky driving behaviors and to examine the relationship between these behaviors and their levels of risk propensity as well as their self-efficacy relative to driving. A cross-sectional, quantitative study was conducted to explore the relationship between the variables. A 63-item survey was completed by 227 rural EMS personnel. The statistical model resulting from this study identifies risky-driving self-efficacy and risk propensity as significant predictors of engaging in risky driving behaviors, with self-efficacy emerging as the strongest predictor. The predictive model fit well within the Social Cognitive Theory construct of triadic reciprocity, providing a platform from which to develop mitigating strategies to foster systemic as well as behavioral changes, while tailoring interventions to highly self-efficacious, risk-taking individuals who gravitate toward risky professions, including rural EMS personnel.

APA, Harvard, Vancouver, ISO, and other styles

10

McGinn, Megan C. "Predicting Factors for Use of Texting and Driving Applications and the Effect on Changing Behaviors." Thesis, Southern Illinois University at Edwardsville, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1557636.

Full text

Abstract:

<p> Cell phone companies are constantly developing faster and more high tech phones in order to satisfy society's demand to carry a miniature computer in their pocket. As society has a continual demand for cell phones, mobile phone companies continue to expand cellular capabilities. One of these advances in cell phone technology is the advent of text messaging. In a survey of 800 teens (ages of 12-17), one in three or 34% between the ages of 16-17, reported they text while driving (Lenhart, Ling, Campbell & Purcell, 2010). Olsen, Hanowski, Hickman and Bocanegra (2009) reported text messaging on cell phones was the most risky behavior when compared with other behaviors such as dialing a cell phone, looking at a map or reaching for another object. A study in 2009 revealed cell phone use was associated with 995 distracted driving fatalities (NHTSA, 2010). This number accounts for approximately 18% of distracted driving related fatalities. Cell phone use was also associated with 24,000 distracted driving injuries, which accounts for 5% of overall distracted driving injuries. The current study seeks to examine what effect a person's attitudes regarding texting and driving, the likelihood of engaging in texting and driving behavior and frequency of reported texting and driving behaviors have on the probability of using a cell phone application designed to prevent texting and driving. The current study also seeks to examine whether downloading a cell phone application has an effect on texting and driving behaviors.</p>

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Driving behaviors"

1

Yu, Jiadi, Yingying Chen, and Xiangyu Xu. Sensing Vehicle Conditions for Detecting Driving Behaviors. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89770-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Jeanette, Goodstein, ed. Who's driving your bus?: Codependent business behaviors of workaholics, perfectionists, martyrs, tap dancers, caretakers & people pleasers. Pfeiffer & Company, 1993.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Diane, Morgan. My dog is driving me crazy!: Be smarter than your dog! : a practical guide to understanding and correcting problem behaviors. TFH Publications, 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

Haigney, Diane. Assessing compensatory behaviour in driving. University of Birmingham, 2000.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Neuenburg, Jesko-Philipp. Market-Driving Behavior in Emerging Firms. Gabler, 2010. http://dx.doi.org/10.1007/978-3-8349-8492-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Corbett, Claire. Unlawful driving behaviour: A criminological perspective. Transport Research Laboratory, 1992.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Employee-driven systems for safe behavior: Integrating behavioral and statistical methodologies. Van Nostrand Reinhold, 1995.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Dorn, Lisa. Individual and group differences in driving behaviour. Aston University. Aston Business School, 1992.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Brewer, Forrest D. Constraint driven behavioral synthesis. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1988.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Diogo, Rui. Evolution Driven by Organismal Behavior. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47581-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Driving behaviors"

1

Craig, Jamie, and Mehrdad Nojoumian. "Should Self-Driving Cars Mimic Human Driving Behaviors?" In HCI in Mobility, Transport, and Automotive Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78358-7_14.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Ding, Linfang, Hongchao Fan, and Liqiu Meng. "Understanding Taxi Driving Behaviors from Movement Data." In Lecture Notes in Geoinformation and Cartography. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16787-9_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Yang, Zhenhai, Meng Yu, Wenfeng Li, Congcong Ma, Raffaele Gravina, and Giancarlo Fortino. "Risk Driving Behaviors Detection Using Pressure Cushion." In Internet and Distributed Computing Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97795-9_15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Schlenoff, Craig, and Michael Gruninger. "Towards a Formal Representation of Driving Behaviors." In Formal Approaches to Agent-Based Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45133-4_30.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Mukai, Naoto, and Misako Hayashi. "Analysis of Driving Behaviors at Roundabout Intersections by Using Driving Simulator." In Intelligent Interactive Multimedia Systems and Services. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19830-9_31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Kato, Takaaki. "Visual Behaviors and Expertise in Race Driving Situation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96074-6_26.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Qiu, Tianjiao. "The Effect of Scanning Behaviors on Marketng Managers’ Representations of Competitive Advantage." In Revolution in Marketing: Market Driving Changes. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11761-4_85.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Sridhar, M. K., and Paul Jeong. "Work Behaviors of Korean and Indian Engineers: A Study of Comparison." In Driving the Economy through Innovation and Entrepreneurship. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0746-7_30.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Kaushik, Meha, and K. Madhava Krishna. "Learning Driving Behaviors for Automated Cars in Unstructured Environments." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11021-5_36.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Rodríguez, Marcela D., Jorge E. Ibarra, José Ruben Roa, Cecilia M. Curlango, Luis Felipe Bedoya, and Héctor Daniel Montes. "Ambient Gamification of Automobile Driving to Encourage Safety Behaviors." In Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13102-3_8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Driving behaviors"

1

Pradhan, Anuj K., Kaigang Li, Johnathon P. Ehsani, Marie Claude Ouimet, Sheila G. Klauer, and Bruce G. Simons-Morton. "Measuring Young Drivers’ Behaviors during Complex Driving Situations." In Driving Assessment Conference. University of Iowa, 2013. http://dx.doi.org/10.17077/drivingassessment.1527.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Hu, Xiaohui, Russell Eberhart, and Brian Foresman. "Modeling drowsy driving behaviors." In 2010 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2010). IEEE, 2010. http://dx.doi.org/10.1109/icves.2010.5550949.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Luo, Gang, Xianping Fu, and Eli Peli. "A Recording and Analysis System of Bioptic Driving Behaviors." In Driving Assessment Conference. University of Iowa, 2009. http://dx.doi.org/10.17077/drivingassessment.1358.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Skaar, Nicole R., and John E. Williams. "Gender Differences in Predicting Unsafe Driving Behaviors in Young Adults." In Driving Assessment Conference. University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1185.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Baicang, Guo, Jin Lisheng, Shi Jian, and Zhang Shunran. "A risky prediction model of driving behaviors: especially for cognitive distracted driving behaviors." In 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 2020. http://dx.doi.org/10.1109/cvci51460.2020.9338665.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Mangalore, Ganesh Pai, Yalda Ebadi, Siby Samuel, Michael Knodler, and Donald Fisher. "Can Virtual Reality Headsets be Used to Measure Accurately Drivers’ Anticipatory Behaviors?" In Driving Assessment Conference. University of Iowa, 2019. http://dx.doi.org/10.17077/drivingassessment.1716.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Campbell, John L., Christian Richard, Randolph Atkins, Monica G. Lichty, and James L. Brown. "Not So Fast! An Investigation of Real-World Speeding Behaviors and Underlying Attitudes." In Driving Assessment Conference. University of Iowa, 2013. http://dx.doi.org/10.17077/drivingassessment.1459.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Hsu, Chih-Chung, Wen-Hai Tseng, and Hao-Ting Yang. "Learning to Predict Risky Driving Behaviors for Autonomous Driving." In 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2020. http://dx.doi.org/10.1109/icce-taiwan49838.2020.9258163.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Ali, Ahmed, Ahmed Elnaggarz, Dirk Reichardtz, and Slim Abdennadher. "Gamified virtual reality driving simulator for asserting driving behaviors." In 2016 1st International Conference on Game, Game Art and Gamification (ICGGAG). IEEE, 2016. http://dx.doi.org/10.1109/icggag.2016.8052668.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Bowers, Alex R., Aaron J. Mandel, Robert B. Goldstein, and Eli Peli. "Simulator-Based Driving with Hemianopia: Detection Performance and Compensatory Behaviors on Approach to Intersections." In Driving Assessment Conference. University of Iowa, 2007. http://dx.doi.org/10.17077/drivingassessment.1248.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Driving behaviors"

1

Huh, Jason, and Julian Reif. Teenage Driving, Mortality, and Risky Behaviors. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27933.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Francfort, Jim. What Were the Driving and Charging Behaviors of High Mileage Accumulators? Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1483587.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1928.

Full text

Abstract:

Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.

APA, Harvard, Vancouver, ISO, and other styles

4

Tavakoli, Arash, Vahid Balali, and Arsalan Heydarian. How do Environmental Factors Affect Drivers’ Gaze and Head Movements? Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2044.

Full text

Abstract:

Studies have shown that environmental factors affect driving behaviors. For instance, weather conditions and the presence of a passenger have been shown to significantly affect the speed of the driver. As one of the important measures of driving behavior is the gaze and head movements of the driver, such metrics can be potentially used towards understanding the effects of environmental factors on the driver’s behavior in real-time. In this study, using a naturalistic study platform, videos have been collected from six participants for more than four weeks of a fully naturalistic driving scenario. The videos of both the participants’ faces and roads have been cleaned and manually categorized depending on weather, road type, and passenger conditions. Facial videos have been analyzed using OpenFace to retrieve the gaze direction and head movements of the driver. Results, overall, suggest that the gaze direction and head movements of the driver are affected by a combination of environmental factors and individual differences. Specifically, results depict the distracting effect of the passenger on some individuals. In addition, it shows that highways and city streets are the cause for maximum distraction on the driver’s gaze.

APA, Harvard, Vancouver, ISO, and other styles

5

Nakatsuka, Fuyuki, Shuji Watanabe, Taro Sekine, et al. Event-Driven Model on Driving Behavior in the Left Turn. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0621.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Nakamura, Takashi, Katsuya Matsunaga, Kazunori Shidoji, and Yuji Matsuki. The Measurement of Everyday Driving Behavior. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0566.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Asao, Takafumi, Takahiro Wada, Shun'ichi Doi, and Kazuyoshi Tsukamoto. Influence of Physical Workloads on Driving Behavior. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0626.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Asao, Takafumi, Takahiro Wada, Shun'chi Doi, and Kazuyoshi Tsukamoto. Analysis of Driving Behavior Under Physical Workloads. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0055.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Kurtz, Jennifer M., Samuel Sprik, Genevieve Saur, and Shaun Onorato. Fuel Cell Electric Vehicle Driving and Fueling Behavior. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1501674.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0305.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!