Journal articles: 'Model of driver'

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Jiang, Haobin, Huan Tian, and Yiding Hua. "Model predictive driver model considering the steering characteristics of the skilled drivers." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401982933. http://dx.doi.org/10.1177/1687814019829337.

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First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers’ steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a β-spline curve.

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Tan, Yun Long, and Hong Fei Jia. "Establishment and Validation of Mainline Driver Type Model at Expressway-Ramp Merging Area." Applied Mechanics and Materials 409-410 (September 2013): 1392–97. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1392.

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The driver characteristic is an important factor that affects driver behaviors, however, the existing driver behavior models little consider the influence of driver own characteristic differences on the driver behaviors. As the driver mental and physical behaviors in the process of driving are uncertainty and ambiguity, the mainline vehicles at expressway-ramp merging area are selected as research object, and the fuzzy clustering theory is introduced. In order to describe the mainline drivers characteristics accurately, the mainline vehicle acceleration, the relative speed of the current mainline vehicle to the all mainline vehicles and the lag gap of the mainline vehicle are selected to cluster by the fuzzy clustering method, and the driver type distribution model is built by K-S test method. Then, the driver type distribution data as a key parameter is incorporated into the expressway merging model, in order to represent the effect of driver characteristic on drive behavior. Finally, the microscopic traffic simulation system MTSS is taken as the simulation plat to build simulation model and validate the built mainline driver type model, the output results from the simulation system are compared with the field data, the satisfactory results indicate that the built driver type model can be used to describe the impact of driver type on driving behavior.

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Feng, Yuxiang, Pejman Iravani, and Chris Brace. "A Fuzzy Logic-Based Approach for Humanized Driver Modelling." Journal of Advanced Transportation 2021 (June 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/4413505.

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All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

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Braghin, F., F. Cheli, S. Melzi, and E. Sabbioni. "Race driver model." Computers & Structures 86, no. 13-14 (July 2008): 1503–16. http://dx.doi.org/10.1016/j.compstruc.2007.04.028.

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Chen, Liang-Kuang, and A. Galip Ulsoy. "Identification of a Driver Steering Model, and Model Uncertainty, From Driving Simulator Data." Journal of Dynamic Systems, Measurement, and Control 123, no. 4 (January 17, 2001): 623–29. http://dx.doi.org/10.1115/1.1409554.

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For active safety systems that function while the driver is still in the control loop, driver uncertainty can affect system performance significantly. In this paper, an approach to obtain both the driver model and its uncertainty from driving simulator data is presented. The structured uncertainty is used to represent the driver’s time-varying behavior, and the unstructured uncertainty for unmodeled dynamics. The uncertainty models can represent both the uncertainty within one driver and the uncertainty across multiple drivers. The structured uncertainty suggests that an estimation and adaptation scheme might be applicable for the design of controllers for active safety systems.

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Mubasher, Mian Muhammad, and Syed Waqar Ul Qounain Jaffry. "Incorporation of the Driver’s Personality Profile in an Agent Model." PROMET - Traffic&Transportation 27, no. 6 (December 21, 2015): 505–14. http://dx.doi.org/10.7307/ptt.v27i6.1675.

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Urban traffic flow is a complex system. Behavior of an individual driver can have butterfly effect which can become root cause of an emergent phenomenon such as congestion or accident. Interaction of drivers with each other and the surrounding environment forms the dynamics of traffic flow. Hence global effects of traffic flow depend upon the behavior of each individual driver. Due to several applications of driver models in serious games, urban traffic planning and simulations, study of a realistic driver model is important. Hhence cognitive models of a driver agent are required. In order to address this challenge concepts from cognitive science and psychology are employed to design a computational model of driver cognition which is capable of incorporating law abidance and social norms using big five personality profile.

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Wen, Huiying, Zuogan Tang, Yuchen Zeng, and Kexiong Zhang. "A Comprehensive Analysis for the Heterogeneous Effects on Driver Injury Severity in Single-Vehicle Passenger Car and SUV Rollover Crashes." Journal of Advanced Transportation 2020 (January 13, 2020): 1–13. http://dx.doi.org/10.1155/2020/1273605.

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In road traffic crashes, although rollover crashes account for a relatively low proportion, those result in a high fatality rate. The present study performed random parameters ordered logit models to examine risk factors as well as their heterogeneous effects on driver injury severity in single-vehicle passenger car and SUV rollover crashes. Crash data for the empirical analysis were extracted from Texas Crash Record Information System (CRIS) database during the year 2016. Model estimation results show that six variables (male drivers, drivers’ age, airbag deployment, failure to drive in single lane, speed limit, and rural area) were found to produce normally distributed parameters in passenger car model, while nine parameters (male drivers, safety belt use, airbag deployment, drug or alcohol use, failure to drive in single lane, improper evasive action, vehicle model year, friday, and rural area) in SUV model were found to be normally distributed. Several other factors with fixed parameters were found to be associated with driver injury severity in single-vehicle passenger car or SUV rollover crashes, most notably: ejection or partial ejection, turning left, intersection, August, adverse weather conditions, and night with light. These variables were significant in both models; most variables have stronger effects on nonincapacitating injury and serious injury outcomes in SUV than in passenger car rollover crashes. These findings provide a deep insight into causality nature and factor involved in driver injury severity in single-vehicle passenger car and SUV rollover crashes and are also helpful for transport agencies to determine appropriate countermeasures aimed at mitigating injuries sustained by drivers in single-vehicle rollover crashes.

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McGordon, A., J. E. W. Poxon, C. Cheng, R. P. Jones, and P. A. Jennings. "Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 225, no. 11 (July 20, 2011): 1518–30. http://dx.doi.org/10.1177/0954407011409116.

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The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.

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Derbel, Oussama, Tamás Péter, Hossni Zebiri, Benjamin Mourllion, and Michel Basset. "Modified Intelligent Driver Model." Periodica Polytechnica Transportation Engineering 40, no. 2 (2012): 53. http://dx.doi.org/10.3311/pp.tr.2012-2.02.

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Kruszewski, Mikołaj, and Mirosław Nader. "Analysis of the attention distraction of inexperienced drivers using a fuzzy model – research results." WUT Journal of Transportation Engineering 125 (June 1, 2019): 53–62. http://dx.doi.org/10.5604/01.3001.0013.6570.

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Limiting the number and consequences of the traffic accidents is one of the most important goals of the EU policy for the road transport. Despite significant efforts in this area, the targets set for the 2010-2020 decade are unlikely to be achieved. This may be due to, inter alia, the increasing importance of the driver attention distraction as a factor contributing to their occurrence. In order to limit the effects of distraction, attempts are made to develop a method to detect such a state of a driver. The distraction of the driver affects the way he drives the vehicle. The authors in their earlier work conducted a research aimed at developing model for detecting states of distraction of the driver's attention, based on a change in the method of vehicle steering. The developed model uses fuzzy logic to detect distraction. This paper presents the results of this model's operation on a sample of 72 drivers, including 36 inexperienced drivers who were the main object of the tests.

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James, Rachel M., and Britton E. Hammit. "Identifying Contributory Factors to Heterogeneity in Driving Behavior: Clustering and Classification Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (May 18, 2019): 343–53. http://dx.doi.org/10.1177/0361198119849404.

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Previous research efforts using aerially collected trajectory-level data have confirmed the existence of inter-driver heterogeneity, where different car-following model (CFM) specifications and calibrated parameter sets are required to adequately capture drivers’ driving behavior. This research hypothesizes that there also exist clusters of drivers whose behavior is sufficiently similar to be considered a homogeneous group. To test this hypothesis, this study applies a 664-trip sample of trajectory-level data from the SHRP2 Naturalistic Driving Study to calibrate the Gipps, Intelligent Driver Model, and Wiedemann 99 CFMs. Using the calibrated parameter coefficients, this research provides evidence of the existence of homogeneous groups of driving behavior using the expectation maximization clustering algorithm. Four classification algorithms are then applied to classify the trip’s cluster ID according to driver demographics. Driver age, income, and marital status were most commonly identified as important classification attributes, while gender, work status, and living status appear less significant. The classification algorithms, which sought to classify a trip’s behavioral cluster ID by the driver-specific attributes, achieved the highest accuracy rate when predicting the desired velocity car-following parameter clusters. This effort illustrates that some drivers drive sufficiently alike to form a cluster of similar behavior; moreover, it was confirmed that driver-specific attributes can be utilized to classify drivers into these homogeneous driver groups.

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Wagner, Marco, Dieter Zöbel, and Ansgar Meroth. "Model-driven development of SOA-based driver assistance systems." ACM SIGBED Review 10, no. 1 (February 2013): 37–42. http://dx.doi.org/10.1145/2492385.2492392.

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Wu, Changxu (Sean), Omer Tsimhoni, and Yili Liu. "Development of an Adaptive Workload Management System using the Queueing Network-Model Human Processor." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 24 (October 2007): 1540–44. http://dx.doi.org/10.1177/154193120705102406.

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Drivers overloaded with information from in-vehicle systems significantly increase the chance of vehicle collisions. Developing adaptive workload management systems (AWMS) to dynamically control the rate of messages from these in-vehicle systems is one of the solutions to this problem. However, existing AWMS do not use driver models to estimate workload, and only suppress or redirect messages without changing the rate of messages from the in-vehicle systems. In this work, we propose a prototype of a new adaptive workload management system, the Queuing Network-Model Human Processor (QN-MHP) AWMS, which includes a model of driver workload based on the queueing network theory of human performance that estimates driver workload in different driving situations and a message controller that dynamically controls the rate of messages presented to drivers. Corresponding experimental studies were conducted to validate the potential effectiveness of this system in reducing driver workload and improving driver performance.

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Liu, Junhui, Yajuan Jia, Yaya Wang, and Petr Dolezel. "Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network." Journal of Advanced Transportation 2020 (September 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/8859891.

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Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system. To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method. This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data. Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained through the whale optimization algorithm. Finally, the well-trained model can be used to represent the human drivers’ operation effectively. The MATLAB simulation results showed that the driver model can achieve an accuracy of 90%.

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Salvucci, Dario D., Erwin R. Boer, and Andrew Liu. "Toward an Integrated Model of Driver Behavior in Cognitive Architecture." Transportation Research Record: Journal of the Transportation Research Board 1779, no. 1 (January 2001): 9–16. http://dx.doi.org/10.3141/1779-02.

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Driving is a multitasking activity that requires drivers to manage their attention among various driving- and non-driving-related tasks. When one models drivers as continuous controllers, the discrete nature of drivers’ control actions is lost and with it an important component for characterizing behavioral variability. A proposal is made for the use of cognitive architectures for developing models of driver behavior that integrate cognitive and perceptual-motor processes in a serial model of task and attention management. A cognitive architecture is a computational framework that incorporates built-in, well-tested parameters and constraints on cognitive and perceptual-motor processes. All driver models implemented in a cognitive architecture necessarily inherit these parameters and constraints, resulting in more predictive and psychologically plausible models than those that do not characterize driving as a multitasking activity. These benefits are demonstrated with a driver model developed in the ACT-R cognitive architecture. The model is validated by comparing its behavior to that of human drivers navigating a four-lane highway with traffic in a fixed-based driving simulator. Results show that the model successfully predicts aspects of both lower-level control, such as steering and eye movements during lane changes, and higher-level cognitive tasks, such as task management and decision making. Many of these predictions are not explicitly built into the model but come from the cognitive architecture as a result of the model’s implementation in the ACT-R architecture.

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SHIMOKOYAMA, Osamu, Yoshiro SUDA, and Daisuke Yamaguchi. "2201 The analysis of driver skill by Driver-Model." Proceedings of the Transportation and Logistics Conference 2009.18 (2009): 243–46. http://dx.doi.org/10.1299/jsmetld.2009.18.243.

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Yang, Junru, Duanfeng Chu, Rukang Wang, Meng Gao, and Chaozhong Wu. "Coupling effect modeling of driver vehicle environment factors influencing speed selections in curves." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 7 (August 27, 2019): 2066–78. http://dx.doi.org/10.1177/0954407019870349.

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It is of significant importance to select an appropriate speed for a vehicle to drive through an upcoming curve. Previous studies have mainly taken into account the vehicle–road interaction, which lacks quantitative analysis of drivers’ driving behavior related to curve speed selections. In this study, a curve speed model derived from the vehicle–road coupling effect analysis is combined with drivers’ driving styles which are classified into aggressive and moderate styles. Moreover, a driver behavior questionnaire based analysis is carried out for quantitative identification of the above two groups of drivers, compared with the traditional vehicle-motion-indexed classification of driving styles. Unlike previous curve speed models, the proposed model not only takes the vehicle–road coupling effect into consideration, but also introduces a driving style factor which is quantified with both driver behavior questionnaire analysis and vehicle-motion-indexed classification. The proposed curve speed model was validated with the road test data. It is found that the proposed curve speed model considering both the vehicle–road interaction and drivers’ driving styles could effectively guarantee traffic safety and riding comfort in sharp curves.

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Bittner, Alvah C., Ozgur Simsek, William H. Levison, and John L. Campbell. "On-Road Versus Simulator Data in Driver Model Development Driver Performance Model Experience." Transportation Research Record: Journal of the Transportation Research Board 1803, no. 1 (January 2002): 38–44. http://dx.doi.org/10.3141/1803-06.

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AMANO, Yasushi, Sueharu NAGIRI, Masatoshi HADA, and Shun-ichi DOI. "Driver Behavior Model in Emergency Situations. On Basic Structure of A Driver Model." Transactions of the Japan Society of Mechanical Engineers Series C 65, no. 633 (1999): 1966–72. http://dx.doi.org/10.1299/kikaic.65.1966.

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Ko, Dongnam, and Enrique Zuazua. "Asymptotic behavior and control of a “guidance by repulsion” model." Mathematical Models and Methods in Applied Sciences 30, no. 04 (March 20, 2020): 765–804. http://dx.doi.org/10.1142/s0218202520400047.

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We model and analyze a guiding problem, where the drivers try to steer the evaders’ positions toward a target region while the evaders always try to escape from drivers. This problem is motivated by the guidance-by-repulsion model [R. Escobedo, A. Ibañez and E. Zuazua, Optimal strategies for driving a mobile agent in a “guidance by repulsion” model, Commun. Nonlinear Sci. Numer. Simul. 39 (2016) 58–72] where the authors answer how to control the evader’s position and what is the optimal maneuver of the driver. First, we analyze well posedness and behavior of the one-driver and one-evader model, assuming of the same friction coefficients. From the long-time behavior, the exact controllability is proved in a long enough time horizon. Then, we extend the model to the multi-driver and multi-evader case. We assumed three interaction rules in the context of collective behavior models: flocking between evaders, collision avoidance between drivers and repulsive forces between drivers and evaders. These interactions depend on the relative distances, and each agent is assumed to be undistinguishable and obtained an averaged effect from the other individuals. In this model, we develop numerical simulations to systematically explore the nature of controlled dynamics in various scenarios. The optimal strategies turn out to share a common pattern to the one-driver and one-evader case: the drivers rapidly occupy the position behind the target, and want to pursuit evaders in a straight line for most of the time. Inspired by this, we build a feedback strategy which stabilizes the direction of evaders.

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Chu, Liang, Jian Chen, and Yan Bo Wang. "A Driver Model for Direction Control in Intelligent Driving Based on Fuzzy Control." Applied Mechanics and Materials 201-202 (October 2012): 422–27. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.422.

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Driver model is the most essential part of intelligent driving system. In Driver-Vehicle- Environment system, a driver is a pre-condition, to correct pedal input and the steering wheel angle position in right time, which makes the vehicle drive in expected path. A new driver model is introduced. Firstly, a real-time algorithm based on dynamic parameters of vehicle body is proposed to estimate body posture in the next step moment. Secondly, vehicle’s posture is compared with the target trajectory to get a deviation quantity. And a correction value for angle at the steering wheel is obtained according to a driver model based on fuzzy gain modulated PID control, which is completed in Simulink workspace. Finally, driver model is verified by two typical driving maneuvers in simulation. And results show that the driver model achieves a good performance and suitability in different applications of Driver-Vehicle-Environment.

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Sun, Chuan, Chaozhong Wu, Duanfeng Chu, Zhenji Lu, Jian Tan, and Jianyu Wang. "A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (July 24, 2018): 1850037. http://dx.doi.org/10.1142/s0218001418500374.

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This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.

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Zhang, Zhaolong, Yuan Zou, Xudong Zhang, Zhifeng Xu, and Han Wang. "Driver Model Based on Optimized Calculation and Functional Safety Simulation." Energies 13, no. 24 (December 17, 2020): 6659. http://dx.doi.org/10.3390/en13246659.

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The simulation of electronic control function failure has been utilized broadly as an evaluation method when determining the Automotive Safety Integrity Level (ASIL). The driver model is quite critical in the ASIL evaluation simulation. A new driver model that can consider drivers of different driving skills is proposed in this paper. It can simulate the overall performance of different drivers driving vehicles by adjusting parameters, with which the impact of a certain electronic control function failure and the ASIL are evaluated. This paper has taken the function failure of regenerative braking as the simulation object in the double-lane-change driving scenario to simulate typical driving conditions with the designed driver model, and then has obtained the ASIL of regenerative braking function, which is applied to a BAIC new energy vehicle development project.

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Chen, Gang, Weigong Zhang, and Bing Yu. "Multibody dynamics modeling of electromagnetic direct-drive vehicle robot driver." International Journal of Advanced Robotic Systems 14, no. 5 (September 1, 2017): 172988141773189. http://dx.doi.org/10.1177/1729881417731896.

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Collaborative dynamics modeling of flexible multibody and rigid multibody for an electromagnetic direct-drive vehicle robot driver is proposed in the article. First, spatial dynamic equations of the direct-drive vehicle robot driver are obtained based on multibody system dynamics. Then, the shift manipulator dynamics model and the mechanical leg dynamics model are established on the basis of the multibody dynamics equations. After establishing a rigid multibody dynamics model and conducting finite element mesh and finite element discrete processing, a flexible multibody dynamics modeling of the electromagnetic direct-drive vehicle robot driver is established. The comparison of the simulation results between rigid and flexible multibody is performed. Simulation and experimental results show the effectiveness of the presented model of the electromagnetic direct-drive vehicle robot driver.

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Reński, Andrzej. "Identification of Driver Model Parameters." International Journal of Occupational Safety and Ergonomics 7, no. 1 (January 2001): 79–92. http://dx.doi.org/10.1080/10803548.2001.11076478.

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Yoshimoto, K. "Active safety and driver model." JSAE Review 16, no. 3 (July 1995): 311. http://dx.doi.org/10.1016/0389-4304(95)95017-o.

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Luo, Qiang, Xinqiang Chen, Jie Yuan, Xiaodong Zang, Junheng Yang, and Jing Chen. "Study and Simulation Analysis of Vehicle Rear-End Collision Model considering Driver Types." Journal of Advanced Transportation 2020 (January 23, 2020): 1–11. http://dx.doi.org/10.1155/2020/7878656.

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The reasonable distance between adjacent cars is very crucial for roadway traffic safety. For different types of drivers or different driving environments, the required safety distance is different. However, most of the existing rear-end collision models do not fully consider the subjective factor such as the driver. Firstly, the factors affecting driving drivers’ characteristics, such as driver age, gender, and driving experience are analyzed. Then, on the basis of this, drivers are classified according to reaction time. Secondly, three main factors affecting driving safety are analyzed by using fuzzy theory, and the new calculation method of the reaction time is obtained. Finally, the improved car-following safety model is established based on different reaction time. The experimental results have shown that our proposed model obtained more accurate vehicle safety distance with varied traffic kinematic conditions (i.e., different traffic states, varied driver types, etc.). The findings can help traffic regulation departments issue early warnings to avoid potential traffic accidents on roads.

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Rodríguez, Daniel A., Marta Rocha, Asad J. Khattak, and Michael H. Belzer. "Effects of Truck Driver Wages and Working Conditions on Highway Safety: Case Study." Transportation Research Record: Journal of the Transportation Research Board 1833, no. 1 (January 2003): 95–102. http://dx.doi.org/10.3141/1833-13.

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The role of human capital and occupational factors in influencing driver safety has gained increased attention from trucking firms and policy makers. The influence of these factors, along with demographic factors, on the crash frequency of truck drivers is examined. A unique driver-level data set from a large truckload company collected over 26 months was used for estimating regression models of crash counts. On the basis of estimates from a zero-inflation Poisson regression model, results suggest that human capital and occupational factors, such as pay, job tenure, and percentage of miles driven during winter months, have a significantly better explanatory power of crash frequency than demographic factors. Relative to the zero-inflation and count models, results suggest that higher pay rates and pay increases are related to lower expected crash counts and to a higher probability of no crashes, all else held equal. Although the data come from one company, the evidence provided is a first step in examining the structural causes of unsafe driving behavior, such as driver compensation. These results may motivate other companies to modify operations and driver hiring practices. Also, the need for a comprehensive study of the relationship between driver compensation and driver safety is demonstrated.

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Kang, Kyungwon, and Hesham A. Rakha. "Modeling Driver Merging Behavior: A Repeated Game Theoretical Approach." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20 (August 23, 2018): 144–53. http://dx.doi.org/10.1177/0361198118792982.

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Various lane-changing models have been developed for use within microscopic traffic simulation software to replicate driver merging behavior. An understanding of human driving behavior, which can be gained through such modeling, will be critical in harmonizing emerging advanced vehicle technology, such as connected automated vehicles, with human drivers. Therefore, it is important to ensure that lane-changing models are clearly understood, appropriately designed, and carefully calibrated. An earlier study by Kang and Rakha proposed and developed a decision-making model for merging maneuvers using a game theoretical approach considering two drivers: the driver of the subject vehicle (DS) in an acceleration lane and the driver of the following lag vehicle (DL) in the target lane. The previous model assumed that the DS and DL decide on an action at the first point only, where the subject and lag vehicles are identified. The current study extends the Kang and Rakha model by introducing the concept of a repeated game, assuming that a lane change decision is made repeatedly to adjust to changes in surrounding conditions. For example, drivers often decide to change their initial decision as a result of conflicts with other drivers. A repeated game helps the proposed model produce more realistic decision-making in the lane-changing process. To evaluate the model, driver decisions at a certain stage, along with accumulated historical decision data, were extracted from Next Generation SIMulation (NGSIM) data. The validation results reveal that the proposed repeated game model produces considerable prediction accuracy (above 75%).

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Guan, Hsin, Li Zeng Zhang, and Xin Jia. "An Optimal Preview Acceleration Driver Model with a Correction Factor for Vehicle Directional Control." Applied Mechanics and Materials 437 (October 2013): 623–28. http://dx.doi.org/10.4028/www.scientific.net/amm.437.623.

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Parameters of the optimal preview acceleration driver model for vehicle directional control are determined by drivers delay/lag time and parameters of the reference model of the controlled vehicle. A moving vehicle is a time-varying and nonlinear system, so it is difficult to obtain accurate parameters of the reference model. If large modeling errors of the reference model occur, the classic driver model cannot ensure the driver/vehicle closed-loop system have a satisfactory performance. In this paper, an improved optimal preview acceleration model with a correction factor was proposed, which is based on sensitivity analysis and MRAC (the model reference adaptive control). Simulation results show that the improved driver model has more satisfactory adaptability and robustness comparing with the classic driver model.

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Misra, Shantanu, Vedika Parvez, Tarush Singh, and E. Chitra. "A Portable Driver Assistance System Headset Using Augmented Reality." International Journal of Engineering & Technology 7, no. 3.6 (July 4, 2018): 294. http://dx.doi.org/10.14419/ijet.v7i3.6.15071.

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Vehicle collision leading to life threatening accidents is a common problem which is incrementing noticeably. This necessitated the need for Driver Assistance Systems (DAS) which helps drivers sense nearby obstacles and drive safely. However, it’s inefficiency in unfavorable weather conditions, overcrowded roads, and low signal penetration rates in India posed many challenges during it’s implementation. In this paper, we present a portable Driver Assistance System that uses augmented reality for it’s working. The headset model comprises of five systems working in conjugation in order to assist the driver. The pedestrian detection module, along with the driver alert system serves to assist the driver in focusing his attention to obstacles in his line of sight. Whereas, the speech recognition, gesture recognition and GPS navigation modules together prevent the driver from getting distracted while driving. In the process of serving these two root causes of accidents, a cost effective, portable and holistic driver assistance system has been developed.

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Cao, Jianyong, Hui Lu, Konghui Guo, and Jianwen Zhang. "A Driver Modeling Based on the Preview-Follower Theory and the Jerky Dynamics." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/952106.

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Based on the preview optimal simple artificial neural network driver model (POSANN), a new driver model, considering jerky dynamics and the tracing error between the real track and the planned path, is established. In this paper, the modeling for the driver-vehicle system is firstly described, and the relationship between weighting coefficients of driver model and system parameters is examined through test data. Secondly, the corresponding road test results are presented in order to verify the vehicle model and obtain the information on drive model and vehicle parameters. Finally, the simulations are carried out via CarSim. Simulation results indicate that the jerky dynamics need to be considered and the proposed new driver model can achieve a better path-following performance compared with the POSANN driver model.

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Huang, Lin, Wenya Lv, Qian Sun, and Chengle Ma. "Discrete Optimization Model and Algorithm for Driver Planning in Periodic Driver Routing Problem." Discrete Dynamics in Nature and Society 2019 (February 7, 2019): 1–10. http://dx.doi.org/10.1155/2019/9476362.

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Workforce planning is an operation management problem in the delivery industry to improve service quality and reliability, and the working attitude and passion of drivers, as the direct implementors of delivery service, affect the service level. Consequently, assigning equal workload for drivers so as to improve drivers’ acceptance is a reasonable and efficient workforce plan for managers. This paper investigates a periodic driver routing problem to explore the relationship between workload differential among drivers and total workload; the objective of the optimization problem is to minimize the total workload. To tackle this problem, we first propose a mixed-integer linear programming model, which can be solved by an off-the-shelf mixed-integer linear programming solver, and use the local branching based method to solve larger instances of the problem. Numerical experiments are conducted to validate the effectiveness and efficiency of the proposed model and solution method, as well as the effect of small workload differential among drivers on the total workload.

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Rauth Bhardwaj, Broto. "Sustainable supply chain management through enterprise resource planning (ERP): a model of sustainable computing." International Journal of Management Science and Business Administration 1, no. 2 (2015): 20–32. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.12.1002.

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Green supply chain management (GSCM) is a driver of sustainable strategy. This topic is becoming increasingly important for both academia and industry. With the increasing demand for reducing carbon foot prints, there is a need to study the drivers of sustainable development. There is also need for developing the sustainability model. Using resource based theory (RBT) the present model for sustainable strategy has been developed. On the basis of data collected, the key drivers of sustainability were developed. We used regression and correlation analysis for developing the final model. The study findings suggest that the drivers of GSCM are the environmental policy and the green human resource management (GHRM). This can be done by providing training for adopting sustainability practices. Besides this, another key driver is the sustainability criteria in supplier selection which was found to be enhancing the outcomes of sustainability. The model has practical and theoretical value as it proposes that management support for implementing the sustainability strategy in the organization is essential. The study also guides the managers for implementing sustainable supply chain management practices in the organization.

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Gabibulayev, Magomed, and Bahram Ravani. "A Stochastic Form of a Human Driver Steering Dynamics Model." Journal of Dynamic Systems, Measurement, and Control 129, no. 3 (February 3, 2005): 322–36. http://dx.doi.org/10.1115/1.2098927.

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This work develops a stochastic form of a human driver model which can be used for simulating vehicle guidance and control. The human motor-control function is complex and can be affected by factors such as driver’s training and experience, fatigue, road conditions, and attention. The variations in these effects become more pronounced in hazardous driving conditions such as in snow and ice. One example of such driving conditions is snow removal operation in highway maintenance, where the use of a stochastic driver model seems to be more desirable. This work evaluates and extends existing models of a human driver including stochastic or statistical considerations related to differences in drivers’ experiences and their conditions as well as variations in the effect of disturbances such as plowing forces. The aim is to develop a simulation environment that can be used in design and evaluation of driver assistance systems for snow removal operation in an Intelligent Transportation System environment.

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Olstam, Johan, Viktor Bernhardsson, Charisma Choudhury, Gerdien Klunder, Isabel Wilmink, and Martijn van Noort. "Modelling Eco-Driving Support System for Microscopic Traffic Simulation." Journal of Advanced Transportation 2019 (December 25, 2019): 1–16. http://dx.doi.org/10.1155/2019/2162568.

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Microscopic traffic simulation is an ideal tool for investigating the network level impacts of eco-driving in different networks and traffic conditions, under varying penetration rates and driver compliance rates. The reliability of the traffic simulation results however rely on the accurate representation of the simulation of the driver support system and the response of the driver to the eco-driving advice, as well as on a realistic modelling and calibration of the driver’s behaviour. The state-of-the-art microscopic traffic simulation models however exclude detailed modelling of the driver response to eco-driver support systems. This paper fills in this research gap by presenting a framework for extending state-of-the-art traffic simulation models with sub models for drivers’ compliance to advice from an advisory eco-driving support systems. The developed simulation framework includes among others a model of driver’s compliance with the advice given by the system, a gear shifting model and a simplified model for estimating vehicles maximum possible acceleration. Data from field operational tests with a full advisory eco-driving system developed within the ecoDriver project was used to calibrate the developed compliance models. A set of verification simulations used to illustrate the effect of the combination of the ecoDriver system and drivers’ compliance to the advices are also presented.

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Li, Meng, Guowei Hua, and Haijun Huang. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit." Sustainability 10, no. 11 (November 19, 2018): 4275. http://dx.doi.org/10.3390/su10114275.

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With the extensive use of smart-phone applications and online payment systems, more travelers choose to participate in ridesharing activities. In this paper, a multi-modal route choice model is proposed by incorporating ridesharing and public transit in a single-origin-destination (OD)-pair network. Due to the presence of ridesharing, travelers not only choose routes (including main road and side road), but also decide travel modes (including solo driver, ridesharing driver, ridesharing passenger, and transit passenger) to minimize travelers’ generalized travel cost (not their actual travel cost due to the existence of car capacity constraints). The proposed model is expressed as an equivalent complementarity problem. Finally, the impacts of key factors on ridesharing behavior in numerical examples are discussed. The equilibrium results show that passengers’ rewards and toll charge of solo drivers on main road significantly affect the travelers’ route and mode choice behavior, and an increase of passengers’ rewards (toll) motivates (forces) more travelers to take environmentally friendly travel modes.

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Fikri, Fikri, Rozmi Ismail, and Fatimah Wati Halim. "The Influence of Personality, Driver Stress and Driver Behavior as Mediator on Road Accident among bus Driver in Riau Province Indonesia." Global Journal of Business and Social Science Review (GJBSSR) Volume 4 (2016: Issue-3) 4, no. 3 (August 12, 2016): 56–62. http://dx.doi.org/10.35609/gjbssr.2016.4.3(8).

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Objective - This study aims to examine the contribution of personality, stress of driver and driver behavior as a mediator on road accident among bus driver in Indonesia. The study adopts a survey method to elicit responses from a sample of 400 bus driver who were selected as a Respondent type. The brief purpose of the paper and illustrate the direction that is taken, whether it is empirical or theoretical testing in analyzing the research subject. Methodology/Technique - The Data collecting using the Big Five Personality questionnaires, Driver Stress Inventory, Driver Behavior questionnaires and Road Accident Inventory. The data collected were analysis confirmatory factor analysis and Structural Equation Model (SEM) Findings - The SEM results show that the model hypothesis predictor index of road accidents have a good match but personality factors do not have affect directly on road accident and the stress of driver and driver behavior have a significant effect on Road accident; therefore the model needs to be re-specified.All of the predictors have influenced for 4% of variance on road accidents. Two predictor variables were accounted for 24% of variance on the behavior of drivers. Stress drivers directly affecting road accidents by (β = .13), and driver behavior (β = .07) .Two predictor variables on the driver behavior also reveals that the personality basis directly affects the behavior of the driver (β = .18), followed by stress of the driver have a direct influence on the behavior of drivers (β = .38). The factor of driver behavior error and lapses have strong effect to road accident Novelty - The implication this study show that there is a need for an intervention program in order to reduce the prevalence of accident involvement due to personality factors. The latter should be focused on managing driving behavior. Type of Paper - Emperical Keywords: Driver Stress, Driver Behavior, Road, Accident, Indonesia.

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Lee, Seolyoung, Jae Hun Kim, Jiwon Park, Cheol Oh, and Gunwoo Lee. "Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data." International Journal of Environmental Research and Public Health 17, no. 24 (December 18, 2020): 9505. http://dx.doi.org/10.3390/ijerph17249505.

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Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

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Hashimoto, Kohjiro, Tetsuyasu Yamada, Takeshi Tsuchiya, Kae Doki, Yuki Funabora, and Shinji Doki. "Detection of contributing object to driving operations based on hidden Markov model." International Journal of Advanced Robotic Systems 16, no. 5 (September 1, 2019): 172988141987679. http://dx.doi.org/10.1177/1729881419876794.

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With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

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Ye, Huixuan, Lili Tu, and Jie Fang. "Predicting Traffic Dynamics with Driver Response Model for Proactive Variable Speed Limit Control Algorithm." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/6181756.

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Variable Speed Limit (VSL) control contributes to potential crash risk reduction by suggesting a suitable dynamic speed limit to achieve more stable and uniform traffic flow. In recent studies, researchers adopted macroscopic traffic flow models and perform prediction-based optimal VSL control. The response of drivers to the advised VSL is one of the most critical parameters in VSL-controlled speed dynamics modeling, which significantly affects the accuracy of traffic state prediction as well as the control reliability and performance. Nevertheless, the variations of driver responses were not explicitly modeled. Thus, in this research, the authors proposed a dynamic driver response model to formulate how the drivers respond to the advised VSL during various traffic conditions. The model was established and calibrated using field data to quantitatively analyze the dynamics of drivers’ desired speed regarding the advised VSL and current traffic state variables. A proactive VSL control algorithm incorporating the established driver response model was designed and implemented in field-data-based simulation study. The design proactive control algorithm modifies VSL in real-time according to the traffic state prediction results, aiming to reduce potential crash risks over the experiment site. By taking into account the real-time driver response variations, the VSL-controlled traffic state dynamics was more accurately predicted. The experimental results illustrated that the proposed control algorithm effectively reduces the crash probabilities in the traffic network.

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Mattsson, Markus T. "Network models of driver behavior." PeerJ 6 (January 10, 2019): e6119. http://dx.doi.org/10.7717/peerj.6119.

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The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

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Derbel, Oussama, Tamas Peter, Hossni Zebiri, Benjamin Mourllion, and Michel Basset. "Modified Intelligent Driver Model for driver safety and traffic stability improvement." IFAC Proceedings Volumes 46, no. 21 (2013): 744–49. http://dx.doi.org/10.3182/20130904-4-jp-2042.00132.

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Boda, Christian-Nils, Esko Lehtonen, and Marco Dozza. "A Computational Driver Model to Predict Driver Control at Unsignalised Intersections." IEEE Access 8 (2020): 104619–31. http://dx.doi.org/10.1109/access.2020.2999851.

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Darban Khales, Sina, Mehmet Metin Kunt, and Branislav Dimitrijevic. "Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit." Canadian Journal of Civil Engineering 47, no. 11 (November 2020): 1249–57. http://dx.doi.org/10.1139/cjce-2019-0394.

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The study analyzed injury severity of teenage and older drivers using 2015–2016 crash data from New Mexico. The fitness of the random-parameter ordered probit models developed for each age group was tested using likelihood ratio, comparing them to a unified model that combines both age groups, as well as comparing the random-parameter to fixed-parameter ordered probit for each age group. In both cases separate random-parameter ordered probit provided better results. It was found that vehicle type and age, lighting condition, alcohol or drug use, speeding, and seatbelt use were significant both for the teenage and older driver injury severity. The weather condition and gender were significant only in the teenage driver model, while driver inattention was significant for older drivers. The impacts of crash factors on injury severity was analyzed using marginal effects. The results indicate notable differences in the effects of contributing factors on driver injury severity between teenage and older drivers, including the sensitivity to changes in the mutual predictor parameter values.

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Ren, Hongze, Yage Guo, Zhonghao Bai, and Xiangyu Cheng. "A Multi-Semantic Driver Behavior Recognition Model of Autonomous Vehicles Using Confidence Fusion Mechanism." Actuators 10, no. 9 (August 31, 2021): 218. http://dx.doi.org/10.3390/act10090218.

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With the rise of autonomous vehicles, drivers are gradually being liberated from the traditional roles behind steering wheels. Driver behavior cognition is significant for improving safety, comfort, and human–vehicle interaction. Existing research mostly analyzes driver behaviors relying on the movements of upper-body parts, which may lead to false positives and missed detections due to the subtle changes among similar behaviors. In this paper, an end-to-end model is proposed to tackle the problem of the accurate classification of similar driver actions in real-time, known as MSRNet. The proposed architecture is made up of two major branches: the action detection network and the object detection network, which can extract spatiotemporal and key-object features, respectively. Then, the confidence fusion mechanism is introduced to aggregate the predictions from both branches based on the semantic relationships between actions and key objects. Experiments implemented on the modified version of the public dataset Drive&Act demonstrate that the MSRNet can recognize 11 different behaviors with 64.18% accuracy and a 20 fps inference time on an 8-frame input clip. Compared to the state-of-the-art action recognition model, our approach obtains higher accuracy, especially for behaviors with similar movements.

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Ghaemi, Sehraneh, Sohrab Khanmohammadi, and Mohammadali Tinati. "Driver's Behavior Modeling Using Fuzzy Logic." Mathematical Problems in Engineering 2010 (2010): 1–29. http://dx.doi.org/10.1155/2010/172878.

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In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model calledModel Iis presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules calledModel IIandModel IIIby using Sugeno fuzzy inference.Model IIandModel IIIhave less linguistic terms thanModel Ifor the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.

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Mafi, Somayeh, Yassir AbdelRazig, and Ryan Doczy. "Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (August 29, 2018): 171–83. http://dx.doi.org/10.1177/0361198118794292.

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Access to non-biased and accurate models capable of predicting driver injury severity of collision events is vital for determining what safety measures should be implemented at intersections. Inadequate models can underestimate the potential for collision events to result in driver fatalities or injuries, which can lead to improperly assessing the safety criteria of an intersection. This study investigates how injury severity differs between drivers of various ages and gender groups using cost-sensitive data-mining models. Previous research efforts have used machine learning methods for predicting injury severity; however, these studies did not consider the consequences (cost) of incorrect predictions. This paper addresses this shortfall by considering the monetary cost of incorrect injury severity predictions when developing C4.5, instance-based (IB), and random forest (RF) machine-learning models. One model of each method was developed for four distinct cohorts of drivers (i.e., younger males, younger females, older males, and older females). Each model considered a selection of driver, vehicular, road/traffic, environmental, and crash parameters for determining if they significantly influenced driver injury severity. A five-year period of two-vehicle crash data collected at signalized intersections in the metropolitan area of Miami, Florida was used in the models. Results indicated that cost-sensitive learning classifiers were superior to regular classifiers at accurately predicting injuries and fatalities of crashes. Among cost-sensitive models, RF outperformed C4.5 and IB models in predicting driver injury severity for four groups of drivers. The models displayed substantial differences in injury severity determinants across the age/gender cohorts.

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Ma, Xiaoyi, Xiaowei Hu, Stephan Schweig, Jenitta Pragalathan, and Dieter Schramm. "A Vehicle Guidance Model with a Close-to-Reality Driver Model and Different Levels of Vehicle Automation." Applied Sciences 11, no. 1 (January 2, 2021): 380. http://dx.doi.org/10.3390/app11010380.

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This paper presents a microscopic vehicle guidance model which adapts to different levels of vehicle automation. Independent of the vehicle, the driver model built is different from the common microscopic simulation models that regard the driver and the vehicle as a unit. The term “Vehicle Guidance Model” covers, here, both the human driver as well as a combination of human driver and driver assistance system up to fully autonomously operated vehicles without a (human) driver. Therefore, the vehicle guidance model can be combined with different kinds of vehicle models. As a result, the combination of different types of driver (human/machine) and different types of vehicle (internal combustion engine/electric) can be simulated. Mainly two parts constitute the vehicle guidance model in this paper: the first part is a traditional microscopic car-following model adjusted according to different degrees of automation level. The adjusted model represents the automation level for the present and the near and the more distant future. The second part is a fuzzy control model that describes how humans adjust the pedal position when they want to reach a target speed with their vehicle. An experiment with 34 subjects was carried out with a driving simulator based on the experimental data and the fuzzy control strategy was determined. Finally, when comparing the simulated model data and actual driving data, it is found that the fuzzy model for the human driver can reproduce the behavior of human participants almost accurately.

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Lonero, Lawrence P., Kathryn M. Clinton, and Douglas M. Black. "Driver Education Curriculum Outline." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 20 (October 1998): 1396–400. http://dx.doi.org/10.1177/154193129804202008.

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The purpose of the AAA Foundation for Traffic Safety outline project was to initiate program development which could lead to “reinventing” a more intensive, comprehensive, and effective driver education system, which could lead to crash reduction in novice drivers. The project reviewed knowledge in a number of areas — driver education effectiveness, novice drivers' needs, and methods of instruction and behavioral influence. The traditional education model used for driver education is inadequate, and fundamental changes in content, methods, and organization are needed. New developments and synergies among education methods, training technologies, organizational change, and demand for quality promise a new and more effective role for driver education in the 21st Century.

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Journal articles on the topic 'Model of driver'

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