Driver Model Research Articles

基于扩展计划行为理论的驾驶员跟驰意向分析

Road traffic flow will show the phenomenon of mixed driving of autonomous vehicles and traditional vehicles in the future. In order to explore psychological factors that affect traditional vehicle drivers' intention to follow autonomous vehicles in the human-machine hybrid driving environment, three variables of driver's understanding, risk perception, and acceptance of autonomous vehicles are introduced to construct an extended theoretical framework of planning behavior. Three hundred thirty-one subjective evaluation data are obtained through questionnaire survey. The internal consistency and reliability of the data are tested by SPSS and AMOS softwares. Finally, the path analysis and mediation effect analysis are carried out by using structural equation model to test relationship among the influencing factors. The results show that the structural equation model of driver's car-following intention based on the theory of extended planning behavior has good explanatory power for the driver's car-following intention in the mixed driving environment. Driver's behavioral attitude, subjective norm and perceived behavioral control of autonomous vehicles have a significant positive and direct effect on driver's car-following intention. Driver's risk perception and acceptance of autonomous vehicles have a significant indirect effect on driver's car-following intention through intermediary variables, in which the effect of risk perception is negative, and the effect of acceptance is positive. The degree of understanding has both a significant positive direct effect and a significant positive indirect effect on driver's car-following intention. The research results can be used as the basis for vehicle interaction behavior analysis in the human-machine hybrid driving environment.

Third-order multi-point preview sliding mode lateral driver model for semi-trailer trains

Third-order multi-point preview sliding mode lateral driver model for semi-trailer trains

Construction of a driver model that can drive multiple courses that reproduce real-world roads

Construction of a driver model that can drive multiple courses that reproduce real-world roads

Research on transformer and long short-term memory neural network car-following model considering data loss.

There is limited research on the loss and reconstruction of car-following features. To delve into car-following's characteristics, we propose a car-following model based on LSTM-Transformer. By fully leveraging the advantages of long short-term memory (LSTM) and transformer models, this study focuses on reconstructing the input car-following features. Training and testing were conducted using 700 car-following segments extracted from a natural driving dataset and the Next Generation Simulation (NGSIM) dataset, and the proposed model was compared with an LSTM model and an intelligent driver model. The results demonstrate that the model performs exceptionally well in feature reconstruction. Moreover, compared to the other two models, it effectively captures the car-following features and accurately predicts the position and speed of the following car when features are lost. Additionally, the LSTM-Transformer model accurately reproduces traffic phenomena, such as asymmetric driving behavior, traffic oscillations and lag, by reconstructing the lost features. Therefore, the LSTM-Transformer car-following model proposed in this study exhibits advantages in feature reconstruction and reproducing traffic phenomena compared to other models.

Loss Function Design for Data-Driven Predictors to Enhance the Energy Efficiency of Connected and Automated Vehicles

In car-following scenarios, accurate previews of the preceding vehicle’s trajectory are essential for minimizing the energy consumption of a following automated vehicle. This work presents a novel design strategy for data-driven vehicle speed predictors to increase the energy efficiency of the following connected and automated vehicle. The loss function is formulated as a weighted-mean-squared error, where the weights are tuned based on the influence of uncertainty at an individual prediction step on the energy consumption of the automated following vehicle. The efficacy of the proposed loss function is validated by applying it to training representative predictors and testing the predictors in the energy-optimal control of the following vehicle. The energy saving of the optimal controller is evaluated by comparing the electricity consumption of a battery electric vehicle with the human driver following, emulated by an intelligent driver model. Simulation results show that the energy consumption of an electric vehicle is reduced by an average of 12% compared to a human driver following demonstrated by an intelligent driver model, whereas using a conventional loss function, a mean-squared error loss function, reduces by 10%.

Personalized Ground Vehicle Lane-Keeping Assist System Design: An Adaptive Sliding Mode Control Formulation With L1+α Reachability

This paper proposes a personalized ground-vehicle lane-keeping assist system (LKA). To begin with, a control-oriented model unifying nonlinear vehicle-road dynamics and a linear driver lane-keeping steering angle model is derived. Subsequently, a novel LKA control strategy, alloying an L1+α -reachable adaptive sliding-mode controller and a shifted-logistic-function-based smooth parameter projection scheme, is formulated. Through a non-quadratic Lyapunov redesign, the sliding manifold is asymptotically reached, throughout which the trajectory of the switching function's value is confined inside the L1+α signal space. In addition, the suggested smooth projection operator can prevent the unbounded control parameter drift whilst maintaining the control command's smoothness. A simulation study employing the CarSim-Simulink joint platform and a cyber driver model is performed to evaluate the proposed personalized LKA adaptive controller and compare it with a linear-robust-control-based solution.

Human-like car-following modeling based on online driving style recognition

<abstract><p>Incorporating human driving style into car-following modeling is critical for achieving higher levels of driving automation. By capturing the characteristics of human driving, it can lead to a more natural and seamless transition from human-driven to automated driving. A clustering approach is introduced that utilized principal component analysis (PCA) and k-means clustering algorithm to identify driving style types such as aggressive, moderate and conservative at the timestep level. Additionally, an online driving style recognition technique is developed based on the memory effect in driving behavior, allowing for real-time identification of a driver's driving style and enabling adaptive control in automated driving. Finally, the Intelligent Driver Model (IDM) has been improved through the incorporation of an online driving style recognition strategy into car-following modeling, resulting in a human-like IDM that emulates real-world driving behaviors. This enhancement has important implications for the field of automated driving, as it allows for greater accuracy and adaptability in modeling human driving behavior and may ultimately lead to more effective and seamless transitions between human-driven and automated driving modes. The results show that the time-step level driving style recognition method provides a more precise understanding of driving styles that accounts for both inter-driver heterogeneity and intra-driver variation. The proposed human-like IDM performs well in capturing driving style characteristics and reproducing driving behavior. The stability of this improved human-like IDM is also confirmed, indicating its reliability and effectiveness. Overall, the research suggests that the proposed model has promising performance and potential applications in the field of automated driving.</p></abstract>

Driver model characterisation at tunnel exit with crosswind using a dynamic driving simulator

Driver model characterisation at tunnel exit with crosswind using a dynamic driving simulator

Third-order multi-point preview sliding mode lateral driver model for semi-trailer trains

Third-order multi-point preview sliding mode lateral driver model for semi-trailer trains

Research on the Steering Torque Control for Intelligent Vehicles Co-Driving With the Penalty Factor of Human–Machine Intervention

In intelligent vehicle cooperative systems, the mismatch in driving characteristics between a human and a machine and the driver misoperation caused by this mismatch result in human–machine conflicts, which significantly affect driving safety. Therefore, an intelligent vehicle human–machine cooperative steering torque control method is proposed herein. To adapt the intelligent system to the varying previewing characteristics of a human, a time-varying previewing driver model is constructed, and a penalty factor for human–machine intervention is designed based on fuzzy rules to assign driving control rights by assessing the driver’s state. Consequently, a human–vehicle–road model with driver preview time and penalty factor as varying parameters is established. Based on gain-scheduling control, a human–machine cooperative steering torque controller is designed to adapt to the varying previewing characteristics of a human and the change in human–machine intervention. The stability and robustness of the entire parameter space are guaranteed by constraining the poles in a certain region. Finally, the proposed human–machine cooperative control scheme demonstrates the effective alleviation of conflicts between the driver and the intelligent driving system.

Mid-Holocene climate simulations over Brazil using the Eta regional paleoclimate model

Paleoclimate simulations are generally performed using coarse global climate models. However, validation of these simulations using a local dataset may be penalized by the coarse grid of the global models. Using a regional climate model, which benefits from the use of higher resolution in a limited area and specific period of time, may help improve the validation of the simulations and help to understand the climate of the past. The objective of this study is to evaluate the Mid-Holocene (MH) simulations for Brazil at 20 km spatial resolution using the regional Eta Model. The simulations are produced by nesting the Eta Model to the National Institute for Space Research (INPE) Global Spectral Atmospheric Model at T062 resolution and 28 vertical levels. Both global and regional models used the same orbital parameters to produce the Milankovic Cycles based on the Berger parameterization. The models adopted the typical carbon dioxide values for the present time and the MH. Downscaling simulations were performed with the Eta Model, resulting in simulations of 2 periods of time, the Eta 0k (present) and the Eta 6k (MH), each 10 years long. The evaluation compares these simulations against proxy data and other paleoclimate model simulations for the region. The difference between the two simulations, 6k and 0k, provides the changes between the two climatic periods. The Eta simulations indicate that the climate during MH was more humid over Northeast Brazil; this agrees partially with paleoclimate data in eastern Northeast Brazil. The Amazon region simulations were mostly drier during MH, in agreement with the paleoclimate data. A weaker convergence of winds in the MH, with winds blowing from the Amazon toward Southeast Brazil, affected the formation and the positioning of the South Atlantic Convergence Zone. Consequently, the associated moisture transport toward the Southeast was smaller; this reduced precipitation in the Brazilian southeastern, central-west, and southern regions, which agrees with the proxy data. The Eta Model simulated a cooler climate for the MH over the Northeast, Central, Southeast, and South Brazil. However, this simulated temperature showed less agreement against proxy data. The Amazon region was slightly warmer, and other transition regions showed no climate change. Overall, the results show that this modified version of the Eta Model is suitable for paleoclimate studies and provides added value over the driver model.

A Microscopic Heterogeneous Traffic Flow Model Considering Distance Headway

The intelligent driver (ID) model characterizes traffic behavior with a constant acceleration exponent and does not follow traffic physics. This results in unrealistic traffic behavior. In this paper, a new microscopic heterogeneous traffic flow model is proposed which improves the performance of the ID model. The forward and lateral distance headways are used to characterize traffic behavior. The stability of the ID and proposed models is examined over a 1000 m circular road with a traffic disturbance after 30 s. The results obtained show that the proposed model is more stable than the ID model. The performance of the proposed and ID models is evaluated over an 1800 m circular road for 150 s with a platoon of 51 vehicles. Results are presented which indicate that traffic evolves realistically with the proposed model. This is because it is based on the lateral distance headway.

Comparison of Transfer Function Models to Represent the Correlation Between Vehicle Lateral Acceleration and Head Tilting Angle in Motion Sickness

Motion Sickness (MS) is described as an unpleasant feeling caused by a forceful movement; hence vehicle movement impacts the severity of MS. While negotiating a curve, drivers and passenger tilt their heads differently, affecting their motion sickness incidence (MSI), which is the severity of MS. MS is a negative feeling, that affects occupant’s comfort, and to further understand the correlation between occupants' behavior and vehicle movement in MS and then represent it using mathematical models, it was proven that MSI could be predicted through mathematical models. However, there is an indefinite value between values between occupant’s behavior and vehicle movement. Based on that it is vital to express it the correlation mathematically. An experiment adopted from a prior study was utilized to get the data and develope the mathematical models with different proportions to represent the correlation between vehicle movement and occupant behavior in motion sickness in transfer function equations using system identification (SI), by utilising black-box feature to use the experimental data as input and output to allow SI to predict the transfer function models. The aim of this study is to investigate MS factors in relation to the vehicle movement and occupant’s behavior, to develop multiple transfer function models, to analyze and compare them. The results were obtained in the three different transfer function orders, second, third and fourth order functions for each proportion used for both the driver and passenger, the driver models’ results were in between 64.68%-67.87%, and the passenger results were in between 63.75%-67.93%, after the comparison the highest fits for each order were obtained. The highest fits amongst driver models were 67.87% (4th Order), 66.78% (3rd Order) and 65.17% (2nd Order) and 67.93% (4th Order), 66.3% (3rd Order) and 64.82% (2nd Order) amongst the passenger models. Those fits were then validated via Simulink with unseen data that was not used in identification process, and lastly the models Root Mean Square Error (RMSE) was obtained for all of them to determine their efficiency.

Anthropogenic sulfate aerosol pollution in South and East Asia induces increased summer precipitation over arid Central Asia

Precipitation has increased across the arid Central Asia region over recent decades. However, the underlying mechanisms of this trend are poorly understood. Here, we analyze multi-model simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) to investigate potential drivers of the observed precipitation trend. We find that anthropogenic sulfate aerosols over remote polluted regions in South and East Asia lead to increased summer precipitation, especially convective and extreme precipitation, in arid Central Asia. Elevated concentrations of sulfate aerosols over remote polluted Asia cause an equatorward shift of the Asian Westerly Jet Stream through a fast response to cooling of the local atmosphere at mid-latitudes. This shift favours moisture supply from low-latitudes and moisture flux convergence over arid Central Asia, which is confirmed by a moisture budget analysis. High levels of absorbing black carbon lead to opposing changes in the Asian Westerly Jet Stream and reduced local precipitation, which can mask the impact of sulfate aerosols. This teleconnection between arid Central Asia precipitation and anthropogenic aerosols in remote Asian polluted regions highlights long-range impacts of anthropogenic aerosols on atmospheric circulations and the hydrological cycle.

Modeling car-following behavior during queue discharge at signalized intersections with countdown timer

ABSTRACT Countdown timers impact driver behavior significantly and are widely used at signalized intersections in many countries. Literature shows potential vehicle class-specific temporal and spatial variations in driver behavior in the presence of timer. Therefore, first, this paper analyzed the temporal, spatial, and vehicle class-specific impacts of timer on driver behavior in heterogeneous traffic conditions based on variations in acceleration. Second, a Long Short-Term Memory model was developed to model vehicle discharge, incorporating the heterogeneity of traffic and impacts of timer. The performance of the model was compared with that of the Intelligent Driver Model using field trajectory data from three signalized intersections in Kerala, India. The model accurately predicts the following behavior with mixed spacing errors of 0.24 to 0.35 and an average mean squared error of spacing of 1.23. The results were better than, or comparable to, results from recent data-driven car-following models and significantly better than IDM.

A fuzzy QFD approach to prioritize capabilities and enablers of organizational agility based on its drivers: a case study

PurposeThe purpose of this paper is to present a fuzzy Quality Function Deployment (QFD)-based approach for identifying and prioritizing organizational agility (OA) capabilities and enablers based on its drivers.Design/methodology/approachFirst, several models for agility drivers (ADs), agility capabilities (ACs) and agility enablers (AEs) are reviewed and ranked, and the best for each one is selected. Second, ADs’ indexes are weighted by using experts’ comments and fuzzy numbers. Finally, by using a proposed fuzzy QFD approach, ACs and AEs are prioritized. In addition, the proposed approach has been examined within a real case study, Golnoor Company in Esfahan, Iran.FindingsResults reveal that among ADs’ criteria, “Changes in competition criteria” have the highest weights for the case study. In addition, “Leadership in the use of current technology” and “Knowledge management” have been ranked as the first place among ACs and AEs, respectively.Originality/valueAfter conducting a comprehensive literature review, the authors did not find any particular framework, which consider AEs and ACs based on ADs simultaneously. Accordingly, the authors’ main novelty is proposing a fuzzy QFD to prioritize the OA capabilities and enablers based on its drivers.

Research on Occupant Safety in Frontal Collision Based on Different Sitting Positions of Intelligent Vehicles

Abstract: To study the safety of drivers with different sitting positions in intelligent vehicle collision, the slide model of SUV was established and the precision of the model was verified by slide test. The simulation model of driver's attitude was established by THUMS. According to the results of post mortem human surrogate (PMHS), the sitting postures of THUMS at different seat backrest angles were adjusted and the drivers’ injuries of three sitting postures in frontal collision were analyzed. Simulation results show that when the driver was in semi-recumbent and reclining posture, the protective effect of the airbag on the head was obviously reduced in collisions, and the head and chest will be seriously damaged due to the large seat backrest angle, resulting in increased distance between the head and the wheel. Besides, the tilting sitting posture leads to the risk of sunken seat in the collision. Therefore, with the development of autonomous driving technology, not only should intelligent vehicles meet occupants’ comfort requirements, but the collision safety caused by the change of sitting postures should be considered.

Impact Evaluation of Cyberattacks on Connected and Automated Vehicles in Mixed Traffic Flow and Its Resilient and Robust Control Strategy.

Connected and automated vehicles (CAVs) present significant potential for improving road safety and mitigating traffic congestion for the future mobility system. However, cooperative driving vehicles are more vulnerable to cyberattacks when communicating with each other, which will introduce a new threat to the transportation system. In order to guarantee safety aspects, it is also necessary to ensure a high level of information quality for CAV. To the best of our knowledge, this is the first investigation on the impacts of cyberattacks on CAV in mixed traffic (large vehicles, medium vehicles, and small vehicles) from the perspective of vehicle dynamics. The paper aims to explore the influence of cyberattacks on the evolution of CAV mixed traffic flow and propose a resilient and robust control strategy (RRCS) to alleviate the threat of cyberattacks. First, we propose a CAV mixed traffic car-following model considering cyberattacks based on the Intelligent Driver Model (IDM). Furthermore, a RRCS for cyberattacks is developed by setting the acceleration control switch and its impacts on the mixed traffic flow are explored in different cyberattack types. Finally, sensitivity analyses are conducted in different platoon compositions, vehicle distributions, and cyberattack intensities. The results show that the proposed RRCS of cyberattacks is robust and can resist the negative threats of cyberattacks on the CAV platoon, thereby providing a theoretical basis for restoring the stability and improving the safety of the CAV.

A Microscopic Traffic Flow Model Characterization for Weather Conditions

Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes traffic behavior based on a constant acceleration exponent resulting in similar traffic behavior regardless of the conditions, which is unrealistic. The ID and proposed models are evaluated over a circular road of length 800 m. The results obtained indicate that the proposed model characterizes the velocity and density better than the ID model. Further, variations in the traffic flow with the proposed model are smaller during adverse weather, as expected. It is also shown that traffic is stable with the proposed model, even during adverse weather.

Driving with a Haptic Guidance System in Degraded Visibility Conditions: Behavioral Analysis and Identification of a Two-Point Steering Control Model

The objective of this study is to determine the ability of a two-point steering control model to account for the influence of a haptic guidance system in different visibility conditions. For this purpose, the lateral control of the vehicle was characterized in terms of driving performance as well as through the identification of anticipation and compensation parameters of the driver model. The hypothesis is that if the structure of the model is valid in the considered conditions, the value of the parameters will change in coherence with the observed behavior. The results of an experiment conducted on a driving simulator demonstrate that the identified model can account for the cumulative influence of the haptic guidance system and degraded visibility. The anticipatory gain is sensitive to changes in driving conditions that have a direct influence on the produced trajectory, and the compensatory gain is sensitive to a decrease in the variability of the lateral position. However, a model with only the steering wheel angle as output is not able to determine whether the change in lateral position variability is due to the driver’s lack of anticipation or to the assistance provided by the haptic guidance system.