Journal articles: 'Driving behaviors'

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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.

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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.

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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.

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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.

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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.

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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.

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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 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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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Ka, Eunhan, Do-Gyeong Kim, Jooneui Hong, and Chungwon Lee. "Implementing Surrogate Safety Measures in Driving Simulator and Evaluating the Safety Effects of Simulator-Based Training on Risky Driving Behaviors." Journal of Advanced Transportation 2020 (June 19, 2020): 1–12. http://dx.doi.org/10.1155/2020/7525721.

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Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about 52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky driving behaviors associated with surrounding vehicles. This study introduces surrogate safety measures (SSMs) into simulator-based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky driving behaviors. The results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. This study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a simulator-based training program using the SSMs is effective in reducing risky driving behaviors. The risky driving behaviors by novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall. The results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management strategies as well as to reduce risky driving behaviors in simulator-based training.

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Mason, John M., Kay Fitzpatrick, Deborah L. Seneca, and Thomas B. Davinroy. "Identification of Inappropriate Driving Behaviors." Journal of Transportation Engineering 118, no. 2 (1992): 281–98. http://dx.doi.org/10.1061/(asce)0733-947x(1992)118:2(281).

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Schultheis, Maria T., Robert J. Matheis, Richard Nead, and John DeLuca. "Driving Behaviors Following Brain Injury." Journal of Head Trauma Rehabilitation 17, no. 1 (2002): 38–47. http://dx.doi.org/10.1097/00001199-200202000-00006.

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Page, Lenore T., Maria Velazquez, and David Claudio. "Collecting Non-Experimental Driving Behaviors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 58, no. 1 (2014): 2190–94. http://dx.doi.org/10.1177/1541931214581460.

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Murphy, S., B. Kane, G. Barr, et al. "401: The Correlation Between Adolescent-Reported Parental Driving Behaviors and Observed Adult Driving Behaviors." Annals of Emergency Medicine 54, no. 3 (2009): S126—S127. http://dx.doi.org/10.1016/j.annemergmed.2009.06.441.

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Qiao, Xuqiang, Ling Zheng, Yinong Li, et al. "Characterization of the Driving Style by State–Action Semantic Plane Based on the Bayesian Nonparametric Approach." Applied Sciences 11, no. 17 (2021): 7857. http://dx.doi.org/10.3390/app11177857.

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The quantification and estimation of the driving style are crucial to improve the safety on the road and the acceptance of drivers with level2–level3(L2–L3) intelligent vehicles. Previous studies have focused on identifying the difference in driving style between categories, without further consideration of the driving behavior frequency, duration proportion properties, and the transition properties between driving style and behaviors. In this paper, a novel methodology to characterize the driving style is proposed by using the State–Action semantic plane based on the Bayesian nonparametric approach, i.e., hierarchical Dirichlet process–hidden semi–Markov model (HDP–HSMM). This method segments the time series driving data into fragment clusters with similar characteristics and construct the State–Action semantic plane based on the statistical characteristics of the state and action layer to label and interpret the fragment clusters. This intuitively and simply visualizes the driving performance of individual drivers, while the risk index of the individual drivers can also be obtained through semantic plane. In addition, according to the joint mutual information maximization (JIMI) approach, seven transition probabilities of driving behaviors are extracted from the semantic plane and applied to identify driving styles of drivers. We found that the aggressive drivers prefer high–risk driving behaviors, and the total duration and frequency of high–risk behaviors are greater than those of cautious and normal drivers. The transition probabilities among high–risk driving behaviors are also greater compared with low–risk behaviors. Moreover, the transition probabilities can provide rich information about driving styles and can improve the classification accuracy of driving styles effectively. Our study has practical significance for the regulation of driving behavior and improvement of road safety and the development of advanced driver assistance systems (ADAS).

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Wang, Yang, Yanyan Chen, and Ning Chen. "Modelling Signal Controlled Traffic Based on Driving Behaviors." Discrete Dynamics in Nature and Society 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/219574.

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In urban traffic, of particular interest the traffic breakdown which is primarily resulted from the driving behaviors is emerged to respond to the traffic signal. To investigate the influences of driving behaviors on the traffic breakdown, a cellular automaton model has been developed by incorporating a number of driving behaviors typically manifesting during the different stages when the vehicle approaching a traffic light. Numerical simulations have been performed based on a road segment consisting of three sections and each section is associated with a set of rules. The numerical simulations have demonstrated that the proposed model is capable of producing the time-delayed traffic breakdown and the dissolution of the oversaturated traffic. Furthermore, it has been evidenced that the probability of the traffic breakdown can be increased by involving the slow-to-start behavior. However, the activation of the anticipatory behavior can effectively impede the transition from undersaturated to oversaturated traffic. Finally, the contributions of the driving behaviors on the traffic breakdown have been quantitatively examined.

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Keiningham, Timothy Lee, Roland T. Rust, Bart Lariviere, Lerzan Aksoy, and Luke Williams. "A roadmap for driving customer word-of-mouth." Journal of Service Management 29, no. 1 (2018): 2–38. http://dx.doi.org/10.1108/josm-03-2017-0077.

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Purpose Managers seeking to manage customer word-of-mouth (WOM) behavior need to understand how different attitudinal drivers (e.g. satisfaction, positive and negative emotion, commitment, and self-brand connection) relate to a range of WOM behaviors. They also need to know how the effects of these drivers are moderated by customer characteristics (e.g. gender, age, income, country). The paper aims to discuss these issues. Design/methodology/approach To investigate these issues a built a large-scale multi-national database was created that includes attitudinal drivers, customer characteristics, and a full range of WOM behaviors, involving both the sending and receiving of both positive and negative WOM, with both strong and weak ties. The combination of sending-receiving, positive-negative and strong ties-weak ties results in a typology of eight distinct WOM behaviors. The investigation explores the drivers of those behaviors, and their moderators, using a hierarchical Bayes model in which all WOM behaviors are simultaneously modeled. Findings Among the many important findings uncovered are: the most effective way to drive all positive WOM behaviors is through maximizing affective commitment and positive emotions; minimizing negative emotions and ensuring that customers are satisfied lowers all negative WOM behaviors; all other attitudinal drivers have lower or even mixed effects on the different WOM behaviors; and customer characteristics can have a surprisingly large impact on how attitudes affect different WOM behaviors. Practical implications These findings have important managerial implications for promotion (which attitudes should be stimulated to produce the desired WOM behavior) and segmentation (how should marketing efforts change, based on segments defined by customer characteristics). Originality/value This research points to the myriad of factors that enhance positive and reduce negative word-of-mouth, and the importance of accounting for customer heterogeneity in assessing the likely impact of attitudinal drivers on word-of-mouth behaviors.

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Yao, Ying, Xiaohua Zhao, Jianming Ma, Chang Liu, and Jian Rong. "Driving Simulator Study: Eco-Driving Training System Based on Individual Characteristics." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (2019): 463–76. http://dx.doi.org/10.1177/0361198119843260.

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This research sought to establish an eco-driving training system based on a driving simulator. The eco-driving training system contained five modules: human machine interface, data management, scene management, mode management, and evaluation algorithm management. It was proposed to base the new eco-driving training system on drivers’ individual characteristics. This system first asked drivers to conduct a diagnostic drive on a stretch of roadway in a driving simulator. The data on each driver’s non-ecological driving behaviors under different events were collected. Then each driver was given a customized training course based on an evaluation of his/her driving behaviors during the diagnostic drive. This training process is called eco-driving training based on individual characteristics (EDTIC). Eighty taxi drivers were recruited and divided into two groups for eco-driving training. One group was trained by watching videos, and the other was trained by the EDTIC training. An analysis of results shows that the EDTIC training was significantly more effective than traditional video training. Under the EDTIC training, all driving behaviors improved and emissions and fuel consumption were greatly reduced; the reduction was as great as 8.3–8.4%. The EDTIC training was proven effective at improving the eco-driving behavior of taxi drivers (i.e., professional drivers), and it could also be employed to train other professional drivers (bus and truck drivers) and non-professional drivers.

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Yao, Ying, Xiaohua Zhao, Hongji Du, Yunlong Zhang, and Jian Rong. "Classification of Distracted Driving Based on Visual Features and Behavior Data using a Random Forest Method." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (2018): 210–21. http://dx.doi.org/10.1177/0361198118796963.

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This research is to explore the relationship between a driver’s visual features and driving behaviors of distracted driving, and a random forest (RF) method is developed to classify driving behaviors and improve the accuracy of detecting distracted driving. Drivers were required to complete four distraction tasks while they followed simulated vehicles in the experiment. In data analysis, the features of distracted driving behaviors are first described, and the visual data are classified into three distraction levels based on the AttenD algorithm. Based on the collected data, this paper shows the relationship between visual features and driving behavior. Significant differences are discovered between different distraction tasks and distraction levels. Additionally, driving behavior data is used to build an RF model to classify distracted driving into three levels. Results demonstrate that this model is feasible to capture the classification of distraction and its accuracy for each distraction task is over 90%. Areas under receiver operating characteristic curve calculated through error-correcting output codes are mainly around 0.9, indicating good reliability. With this classification method, distraction levels could be classified with vehicle operation characteristics. The model established by this method could detect distractions in actual driving through the detection of driving behavior without the need of eye tracking systems.

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YUAN, GUOYONG, ZHICHENG FENG, AIGUO XU, GUANGRUI WANG, and SHAOYING CHEN. "DYNAMICS IN EXCITABLE MEDIA SUBJECTED TO A SPECIFIC SPATIOTEMPORAL WAVE UNDER TWO SCHEMES." International Journal of Bifurcation and Chaos 22, no. 06 (2012): 1250148. http://dx.doi.org/10.1142/s0218127412501489.

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The dynamics in excitable media driven by a specific spatiotemporal wave are studied by investigating wave states, the motion of tips and synchronized behaviors. We demonstrate that multiple-armed spiral waves can be generated in excitable media with rest initial conditions by directly injecting a rigidly rotating spiral wave, also that the meandering driver can induce the spiral wave, with the wider excited parts and the same frequency as the driving wave, in the driven system. It is more interesting to find that the higher similarity between the driving and driven waves occurs when the driving strength is smaller. We also study the dynamics of spiral waves in the driven system when the external driving wave is introduced by the form of difference, and find that the stronger synchronized behaviors appear when the driving strength is larger. The dynamical behaviors can be understood by considering various characteristics of the excitable system, for example the existence of "refractory period" and "vulnerable period" and so on.

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Rengifo, Carolina, Jean-Rémy Chardonnet, Hakim Mohellebi, Damien Paillot, and Andras Kemeny. "Driving simulator study of the relationship between motion strategy preference and self-reported driving behavior." SIMULATION 97, no. 9 (2021): 619–33. http://dx.doi.org/10.1177/0037549721999716.

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Faithful motion restitution in driving simulators normally focuses on track monitoring and maximizing the platform workspace by leaving aside the principal component—the driver. Therefore, in this work we investigated the role of the motion perception model on motion cueing algorithms from a user’s viewpoint. We focused on the driving behavior influence regarding motion perception in a driving simulator. Participants drove a driving simulator with two different configurations: (a) using the platform dynamic model and (b) using a supplementary motion perception model. Both strategies were compared and the participants’ data were classified according to the strategy they preferred. To this end, we developed a driving behavior questionnaire aiming at evaluating the self-reported driving behavior influence on participants’ motion cueing preferences. The results showed significant differences between the participants who chose different strategies and the scored driving behavior in the hostile and violations factors. In order to support these findings, we compared participants’ behaviors and actual motion driving simulator indicators such as speed, jerk, and lateral position. The analysis revealed that motion preferences arise from different reasons linked to the realism or smoothness in motion. Also, strong positive correlations were found between hostile and violation behaviors of the group who preferred the strategy with the supplementary motion perception model, and objective measures such as jerk and speed on different road segments. This indicates that motion perception in driving simulators may depend not only on the type of motion cueing strategy, but may also be influenced by users’ self-reported driving behaviors.

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Muslim, Nurul Hidayah Binti, Arezou Shafaghat, Ali Keyvanfar, and Mohammad Ismail. "Green Driver: driving behaviors revisited on safety." Archives of Transport 47, no. 3 (2018): 49–78. http://dx.doi.org/10.5604/01.3001.0012.6507.

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Interactions between road users, motor vehicles, and environment affect to driver’s travel behavior; however, frailer of proper interaction may lead to ever-increasing road crashes, injuries and fatalities. The current study has generated the green driver concept to evaluate the incorporation of green driver to negative outcomes reduction of road transportation. The study aimed to identify the green driver’s behaviors affecting safe traveling by engaging two research phases. Phase one was to identify the safe driving behaviors using Systematic literature review and Content Analysis methods. Phase one identified twenty-four (24) sub-factors under reckless driving behaviors cluster, and nineteen (19) sub-factors under safe driving practice cluster. Second phase was to establish the actual weight value of the sub-factors using Grounded Group Decision Making (GGDM) and Value Assignment (VA) methods, in order to determine the value impact of each sub-factor to green driving. Phase two resulted that sub-factors Exceeding speed limits (DB f2.2.) and Driver’s cognitive and motor skills (SD f1.2.2.) have received highest actual values, 0.64 and 0.49, respectively; ranked as the High contributor grade. Contrary, the sub-factors Age cognitive decline (DB f1.2.) and Competitive attitude (DB f1.2.), and Avoid gear snatching (SD f1.1.4.) have the lowest actual values; and ranked in low-contribution grade. The rest of the sub-factors have ranked in medium-contribution grade. The research also found out drivers’ personalities (included, physical and psychological characteristics) remains unaccountable and non-measureable yet in driver travel behavior assessment models. The study outputs would be used in development of Green Driver Index Assessment Model.

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David, Ruth, Sandra Rothe, and Dirk Söffker. "State Machine Approach for Lane Changing Driving Behavior Recognition." Automation 1, no. 1 (2020): 68–79. http://dx.doi.org/10.3390/automation1010006.

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Research in understanding human behavior is a growing field within the development of Advanced Driving Assistance Systems (ADASs). In this contribution, a state machine approach is proposed to develop a driving behavior recognition model. The state machine approach is a behavior model based on the current state and a given set of inputs. Transitions to different states occur or we remain in the same state producing outputs. The transition between states depends on a set of environmental and driving variables. Based on a heuristic understanding of driving situations modeled as states, as well as one of the related actions modeling the state, using an assumed relation between them as the state machine topology, in this paper, a crisp approach is applied to adapt the model to real behaviors. An important aspect of the contribution is to introduce a trainable state machine-based model to describe drivers’ lane changing behavior. Three driving maneuvers are defined as states. The training of the model is related to the definition/tuning of transition variables (and state definitions). Here, driving data are used as the input for training. The non-dominated sorting genetic algorithm II is used to generate the optimized transition threshold. Comparing the data of actual human driving behaviors collected using driving simulator experiments and the calculated driving behaviors, this approach is able to develop a personalized behavior recognition model. The newly established algorithm presents an easy to apply, reliable, and interpretable AI approach.

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Yang, Xiaonan, Jung Hyup Kim, and Roland Nazareth. "Hierarchical Task Analysis for Driving under Divided Attention." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 1744–48. http://dx.doi.org/10.1177/1071181319631022.

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Although researchers have made various models of driving behavior, the behavior model under divided attention is not well studied. In this paper, the driver’s behavior differences under divided-attention were studied in a simulated driving environment. A driving scenario was developed to simulate hazards on the highway in dynamic driving conditions. Based on crash and non-crash cases through eye tracking videos from the experiment, Hierarchical task analysis (HTA) was conducted, and decomposed different complex driving behaviors into drivers’ perception, cognition, and decision. Also, their reaction times were compared by using the cognitive-perceptual model in GOMS. Through this study, different driving behaviors and corresponding cognitive factors, which contributed to a slower reaction were identified. The results from this study could be as a valuable input to develop advanced driver assistance systems which could provide smart collision warnings based on the driver’s attention.

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Wang, Jing, ZhongCheng Wu, Fang Li, and Jun Zhang. "A Data Augmentation Approach to Distracted Driving Detection." Future Internet 13, no. 1 (2020): 1. http://dx.doi.org/10.3390/fi13010001.

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Distracted driving behavior has become a leading cause of vehicle crashes. This paper proposes a data augmentation method for distracted driving detection based on the driving operation area. First, the class activation mapping method is used to show the key feature areas of driving behavior analysis, and then the driving operation areas are detected by the faster R-CNN detection model for data augmentation. Finally, the convolutional neural network classification mode is implemented and evaluated to detect the original dataset and the driving operation area dataset. The classification result achieves a 96.97% accuracy using the distracted driving dataset. The results show the necessity of driving operation area extraction in the preprocessing stage, which can effectively remove the redundant information in the images to get a higher classification accuracy rate. The method of this research can be used to detect drivers in actual application scenarios to identify dangerous driving behaviors, which helps to give early warning of unsafe driving behaviors and avoid accidents.

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Okafuji, Yuki, Takahiro Wada, Toshihito Sugiura, Kazuomi Murakami, and Hiroyuki Ishida. "Drivers’ Gaze Behaviors are Influenced by Vehicle Position." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 1625–29. http://dx.doi.org/10.1177/1071181320641393.

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Drivers’ gaze behaviors in naturalistic and simulated driving tasks have been investigated for decades. Many studies focus on driving environment to explain a driver’s gaze. However, if there is a great need to use compensatory steering for lane-keeping, drivers could preferentially acquire information directly required for the task. Therefore, we assumed that a driver’s gaze behavior was influenced not only by the environment but also the vehicle position, especially the lateral position. To verify our hypothesis, we carried out a long-time driving simulator experiment, and the gaze behaviors of two participating drivers were analyzed. Results showed that gaze behavior—the fixation distance and the lateral deviation of the fixation—was influenced by the lateral deviation of the vehicle. Consequently, we discussed processes that determined drivers’ gaze behaviors.

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Pulvirenti, Giulia, Natalia Distefano, Salvatore Leonardi, and Tomaz Tollazzi. "Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors." Safety 7, no. 1 (2021): 20. http://dx.doi.org/10.3390/safety7010020.

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This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.

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Lin, Rui, Yi Li, and Minmin Luo. "A Neural Circuit Driving Maternal Behaviors." Neuron 98, no. 1 (2018): 6–8. http://dx.doi.org/10.1016/j.neuron.2018.03.025.

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Mitra-Sarkar, Sheila, and Marie Andreas. "Driving Behaviors, Risk Perceptions, and Stress." Transportation Research Record: Journal of the Transportation Research Board 2138, no. 1 (2009): 42–45. http://dx.doi.org/10.3141/2138-07.

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GRUBE, JOEL W., and ROBERT B. VOAS. "Predicting underage drinking and driving behaviors." Addiction 91, no. 12 (1996): 1843–57. http://dx.doi.org/10.1046/j.1360-0443.1996.911218438.x.

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Merrikhpour, Maryam, and Birsen Donmez. "Towards Mitigating Teenagers’ Distracted Driving Behaviors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 1879–83. http://dx.doi.org/10.1177/1541931213601428.

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Distraction contributes significantly to teens’ crash risks. Previous studies show that feedback can help mitigate distraction among young and adult drivers; however, the type of feedback that is effective for teenagers remains unexamined. This paper investigates whether real-time and post-drive feedback can mitigate teens’ driver distraction and reports preliminary findings from an ongoing simulator study. Data reported was collected in a between-subjects experiment with three conditions: real-time (n= 8), post-drive (n= 8), and no feedback (n= 9). Real-time feedback was provided as auditory warnings when teens had long offroad glances (>2 sec). Post-drive feedback was an end-of-trip report on teens’ off-road glances and driving performance provided on an in-vehicle display. Compared to no feedback, real-time feedback resulted in significantly smaller number of long off-road glances (>2 sec), smaller average duration of off-road glances, and smaller standard deviation of lane position. The effects observed for post-drive feedback were relatively minor.

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Shams, Mohsen, and Vafa Rahimi-Movaghar. "Risky Driving Behaviors in Tehran, Iran." Traffic Injury Prevention 10, no. 1 (2009): 91–94. http://dx.doi.org/10.1080/15389580802492280.

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Liu, Siyuan, Lionel M. Ni, and Ramayya Krishnan. "Fraud Detection From Taxis' Driving Behaviors." IEEE Transactions on Vehicular Technology 63, no. 1 (2014): 464–72. http://dx.doi.org/10.1109/tvt.2013.2272792.

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Wang, Kunfeng, Wuling Huang, Bin Tian, and Ding Wen. "Measuring Driving Behaviors from Live Video." IEEE Intelligent Systems 27, no. 5 (2012): 75–80. http://dx.doi.org/10.1109/mis.2012.100.

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GRUBE, JOEL W., and ROBERT B. VOAS. "Predicting underage drinking and driving behaviors." Addiction 91, no. 12 (1996): 1843–57. http://dx.doi.org/10.1111/j.1360-0443.1996.tb03813.x.

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Chen, Depeng, Zhijun Chen, Yishi Zhang, Xu Qu, Mingyang Zhang, and Chaozhong Wu. "Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model." Journal of Advanced Transportation 2021 (June 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/6687378.

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In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles.

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Alzayani, Salman, and Randah R. Hamadeh. "Risky Driving Behaviors among Medical Students in the Middle East." International Journal for Innovation Education and Research 3, no. 3 (2015): 42–49. http://dx.doi.org/10.31686/ijier.vol3.iss3.326.

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A cross sectional study was conducted on medical students enrolled in the Arabian Gulf University in the Kingdom of Bahrain. The objective was to describe medical students’ risk-taking behaviors while driving and to provide recommendations for promoting safe driving behaviors among them. A self administered anonymous questionnaire was used, which included questions on demography and driving behaviors. Medical students demonstrated risk taking behaviors while driving, as 40.1% of them did not use their seatbelts, 49.6% speeded (>100km/hr), 54.7% talked on their mobile phones and 45.9% wrote/read text messages while driving. Female students had lower driving risk taking behaviors compared to males. Driving risk taking behaviors declined as students progressed in their medical years. Saudi and Kuwaiti students had more risk taking behaviors than other nationalities. Driving risk taking behaviors cluster among students according to gender, medical year and nationality. Urgent interventions are needed to promote safe driving behaviors among students.

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Wang, Pengwei, Song Gao, Liang Li, Shuo Cheng, and Hailan Zhao. "Research on driving behavior decision making system of autonomous driving vehicle based on benefit evaluation model." Archives of Transport 53, no. 1 (2020): 21–36. http://dx.doi.org/10.5604/01.3001.0014.1740.

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Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

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Al-Shihabi, Talal, and Ronald R. Mourant. "Toward More Realistic Driving Behavior Models for Autonomous Vehicles in Driving Simulators." Transportation Research Record: Journal of the Transportation Research Board 1843, no. 1 (2003): 41–49. http://dx.doi.org/10.3141/1843-06.

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Autonomous vehicles are perhaps the most encountered element in a driving simulator. Their effect on the realism of the simulator is critical. For autonomous vehicles to contribute positively to the realism of the hosting driving simulator, they need to have a realistic appearance and, possibly more importantly, realistic behavior. Addressed is the problem of modeling realistic and humanlike behaviors on simulated highway systems by developing an abstract framework that captures the details of human driving at the microscopic level. This framework consists of four units that together define and specify the elements needed for a concrete humanlike driving model to be implemented within a driving simulator. These units are the perception unit, the emotions unit, the decision-making unit, and the decision-implementation unit. Realistic models of humanlike driving behavior can be built by implementing the specifications set by the driving framework. Four humanlike driving models have been implemented on the basis of the driving framework: ( a) a generic normal driving model, ( b) an aggressive driving model, ( c) an alcoholic driving model, and ( d) an elderly driving model. These driving models provide experiment designers with a powerful tool for generating complex traffic scenarios in their experiments. These behavioral models were incorporated along with three-dimensional visual models and vehicle dynamics models into one entity, which is the autonomous vehicle. Subjects perceived the autonomous vehicles with the described behavioral models as having a positive effect on the realism of the driving simulator. The erratic driving models were identified correctly by the subjects in most cases.

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Lee, Soonyeol, and Soonchul Lee. "Mediating effect of coping behavior on the relationship between driving stress and traffic accident risk." Korean Journal of Industrial and Organizational Psychology 24, no. 4 (2011): 673–93. http://dx.doi.org/10.24230/kjiop.v24i4.673-693.

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The present study was conducted to determine the effects of driving stress on traffic accident risk. Specifically, this study verified the effects of driving stress on drivers' coping behaviors and the aptitude of mediating models through which coping behavior types affect traffic accident risk. As a result, driving stress directly increased traffic accident risk and indirectly affected them through(good and bad) coping behavior types. This indicates that driving stress directly and indirectly affect traffic accident risk by the medium of(good and bad) coping behavior types in multilateral ways.(Commercial and leisure-purposed) driving purposes showed significant differences in the relations between driving stress and traffic accident risk. Specifically, commercial drivers were affected by driving stress, compared to leisure-purposed drivers. As they were unable to defer or abandon driving even under driving stress, commercial drivers responded to the stress more sensitively and increased traffic accident risk by selecting inappropriate(bad) coping behaviors. The results show that the mere concentration on driving stress management cannot sufficiently lower the traffic accident risks caused by driving stress. This is because driving stress have indirect influences on traffic accident risk. Hence, it will be necessary to seek how to reduce driving stress and control coping behavior types in order to lower the traffic accidents risk by the stress.

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Sun, Chuan, Bijun Li, Yicheng Li, and Zhenji Lu. "Driving Risk Classification Methodology for Intelligent Drive in Real Traffic Event." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 09 (2019): 1950014. http://dx.doi.org/10.1142/s0218001419500149.

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To solve the problem that existing driving data cannot correlate to the large number of vehicles in terms of driving risks, is the functionality of intelligent driving algorithm should be improved. This paper deeply explores driving data to build a link between massive driving data and a large number of sample vehicles for driving risk analysis. It sorted out certain driving behavior parameters in the driving data, and extracted some parameters closely related to the driving risk; it further utilized the principal component analysis and factor analysis in spatio-temporal data to integrate certain extracted parameters into factors that are clearly related to the specific driving risks; then, it selected factor scores of driving behaviors as indexes for hierarchical clustering, and obtained multi-level clustering results of the driving risks of corresponding vehicles; in the end, it interpreted the clustering results of the vehicle driving risks. According to the results, it is found that cluster for different risks proposed in this paper for driving behaviors is effective in the hierarchical cluster for typical driving behaviors and it also offers a solution for risk analyses between driving data and large sample vehicles. The results provide the basis for training on safe driving for the key vehicles, and the improvement of advanced driver assistance system, which shows a wide application prospect in the field of intelligent drive.

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López, Joaquín, Pablo Sánchez-Vilariño, Rafael Sanz, and Enrique Paz. "Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework." Sensors 20, no. 2 (2020): 449. http://dx.doi.org/10.3390/s20020449.

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Most autonomous car control frameworks are based on a middleware layer with several independent modules that are connected by an inter-process communication mechanism. These modules implement basic actions and report events about their state by subscribing and publishing messages. Here, we propose an executive module that coordinates the activity of these modules. This executive module uses hierarchical interpreted binary Petri nets (PNs) to define the behavior expected from the car in different scenarios according to the traffic rules. The module commands actions by sending messages to other modules and evolves its internal state according to the events (messages) received. A programming environment named RoboGraph (RG) is introduced with this architecture. RG includes a graphical interface that allows the edition, execution, tracing, and maintenance of the PNs. For the execution, a dispatcher loads these PNs and executes the different behaviors. The RG monitor that shows the state of all the running nets has proven to be very useful for debugging and tracing purposes. The whole system has been applied to an autonomous car designed for elderly or disabled people.

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Qian, Huihuan, Yongsheng Ou, Xinyu Wu, Xiaoning Meng, and Yangsheng Xu. "Support Vector Machine for Behavior-Based Driver Identification System." Journal of Robotics 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/397865.

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We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.

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Fabiano, Gregory A., Nicole K. Schatz, Kevin F. Hulme, et al. "Positive Bias in Teenage Drivers With ADHD Within a Simulated Driving Task." Journal of Attention Disorders 22, no. 12 (2015): 1150–57. http://dx.doi.org/10.1177/1087054715616186.

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Objective: Youth with ADHD exhibit positive bias, an overestimation of ability, relative to external indicators. The positive bias construct is understudied in adolescents, particularly in the domain of driving. Study is needed as youth with ADHD experience greater negative outcomes in driving relative to typically developing teens. Method: Positive bias on a driving simulator task was investigated with 172 teenagers with ADHD, combined type. Youth participated in a driving simulation task and rated driving performance afterward. Results: Compared with external ratings of driving performance, youth overestimated driving competence for specific driving behaviors as well as globally. The global rating demonstrated a greater degree of positive bias. Greater positive bias on global ratings of driving ability also predicted greater rates of risky driving behaviors during the simulator exercise independent from disruptive behavior disorder symptoms. Conclusion: Results inform prevention and intervention efforts for teenage drivers with ADHD.

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Festa, Elena K., Brian R. Ott, Kevin J. Manning, Jennifer D. Davis, and William C. Heindel. "Effect of Cognitive Status on Self-Regulatory Driving Behavior in Older Adults." Journal of Geriatric Psychiatry and Neurology 26, no. 1 (2013): 10–18. http://dx.doi.org/10.1177/0891988712473801.

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Previous findings that older drivers engage in strategic self-regulatory behaviors to minimize perceived safety risks are primarily based on survey reports rather than actual behavior. This study analyzed in-car video recording of naturalistic driving of 18 patients with Alzheimer disease (AD) and 20 age-matched controls in order to (1) characterize self-regulatory behaviors engaged by older drivers and (2) assess how behaviors change with cognitive impairment. Only participants who were rated “safe” on a prior standardized road test were selected for this study. Both groups drove primarily in environments that minimized the demands on driving skill and that incurred the least risk for involvement in major crashes. Patients with AD displayed further restrictions of driving behavior beyond those of healthy elderly individuals, suggesting additional regulation on the basis of cognitive status. These data provide critical empirical support for findings from previous survey studies indicating an overall reduction in driving mobility among older drivers with cognitive impairment.

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Aghabayk, Kayvan, Leila Mashhadizade, and Sara Moridpour. "Need Safer Taxi Drivers? Use Psychological Characteristics to Find or Train!" Sustainability 12, no. 10 (2020): 4206. http://dx.doi.org/10.3390/su12104206.

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Professional drivers play a key role in urban road network safety. It is therefore important to employ safer drivers, also find the problem, and train the existing ones. However, a direct driving test may not be very useful solely because of drivers’ consciousness. This study introduces a latent predictor to expect driving behaviors, by finding the relation between taxi drivers’ psychological characteristics and their driving behaviors. A self-report questionnaire was collected from 245 taxi drivers by which their demographic characteristics, psychological characteristics, and driving behaviors were obtained. The psychological characteristics include instrumental attitude, subjective norm, sensation seeking, aggressive mode, conscientiousness, life satisfaction, premeditation, urgency, and selfishness. Driving behaviors questionnaire (DBQ) provides information regarding drivers’ violations, aggressive violations, errors, and lapses. The standard linear regression model is used to determine the relationship between driving behavior and psychological characteristics of drivers. The findings show that social anxiety and selfishness are the best predictors of the violations; aggressive mode is a significant predictor of the aggressive violations; urgency has a perfect impact on the errors; and finally, life satisfaction, sensation seeking, conscientiousness, age, and urgency are the best predictors of the lapses.

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Dunn, Naomi J., Thomas A. Dingus, Susan Soccolich, and William J. Horrey. "Investigating the impact of driving automation systems on distracted driving behaviors." Accident Analysis & Prevention 156 (June 2021): 106152. http://dx.doi.org/10.1016/j.aap.2021.106152.

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Morgenroth, Thekla, Cordelia Fine, Michelle K. Ryan, and Anna E. Genat. "Sex, Drugs, and Reckless Driving." Social Psychological and Personality Science 9, no. 6 (2017): 744–53. http://dx.doi.org/10.1177/1948550617722833.

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We investigated whether risk-taking measures inadvertently focus on behaviors that are more normative for men, resulting in the overestimation of gender differences. Using a popular measure of risk-taking (Domain-Specific Risk-Taking) in Study 1 ( N = 99), we found that conventionally used behaviors were more normative for men, while, overall, newly developed behaviors were not. In Studies 2 ( N = 114) and 3 ( N = 124), we demonstrate that differences in normativity are reflected in gender differences in self-reported risk-taking, which are dependent on the specific items used. Study 3 further demonstrates that conventional, masculine risk behaviors are perceived as more risky than newly generated, more feminine items, even when risks are matched. We conclude that there is confirmation bias in risk-taking measurement.

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Zhang, Lanfang, Boyu Cui, Minhao Yang, Feng Guo, and Junhua Wang. "Effect of Using Mobile Phones on Driver’s Control Behavior Based on Naturalistic Driving Data." International Journal of Environmental Research and Public Health 16, no. 8 (2019): 1464. http://dx.doi.org/10.3390/ijerph16081464.

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Distracted driving behaviors are closely related to crash risk, with the use of mobile phones during driving being one of the leading causes of accidents. This paper attempts to investigate the impact of cell phone use while driving on drivers’ control behaviors. Given the limitation of driving simulators in an unnatural setting, a sample of 134 cases related to cell phone use during driving were extracted from Shanghai naturalistic driving study data, which provided massive unobtrusive data to observe actual driving process. The process of using mobile phones was categorized into five operations, including dialing, answering, talking and listening, hanging up, and viewing information. Based on the concept of moving time window, the variation of the intensity of control activity, the sensitivity of control operation, and the stability of control state in each operation were analyzed. The empirical results show strong correlation between distracted operations and driving control behavior. The findings contribute to a better understanding of drivers’ natural behavior changes with using mobiles, and can provide useful information for transport safety management.

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Journal articles on the topic 'Driving behaviors'

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