Modeling driver steering behavior in restricted-preview boundary-avoidance tasks

Automated vehicle steering control systems have great potential to improve road safety. The development of such systems calls for mathematical driver models able to represent human drivers' steering behavior in response to automated steering intervention. This article concerns the experimental evaluation of a game-theoretic driver steering control model. The driver model centers on a steering control strategy developed based on the Nash equilibrium of a theoretic noncooperative game between the driver and automated steering controller. The key parameters of the game-theoretic driver model are identified by fitting the model to real driver steering behavior measured from six driver subjects in an experiment using a driving simulator. The game-theoretic driver model is evaluated by compared to a ``conventional'' optimal-control-theoretic driver model, and analyzing their model fitting errors. Results from the analysis demonstrate that the game-theoretic driver model is statistically significantly better than the conventional driver model for representing three out of the six subjects' steering behavior. For the other three subjects, both the two models perform statistically equivalently well.

In this paper, a path tracking control for active rear steering vehicles considering driver steering characteristics is proposed to help drivers track the desired vehicle trajectory in a tailored way. Firstly, the driver model is established, and based on the driver steering data, the driver parameters are identified by the least square method. Based on driver model parameter identification, driver and vehicle models are formed for the MPC controller design. In MPC controller design, global objective function, considering the task performance, vehicle stability, driver's physical workloads, mental workloads and actuator performance, is designed to optimize the overall performance of the driver-vehicle system. On the constraint, the driver operating limit, actuator physical limit and vehicle stability performance limit are also considered to make the controller feasible to in the real practice. Simulation results under different conditions show that the controller can significantly improve the system performance and reduce the driver's workloads.

The aim of this work is to develop a comprehensive yet practical driver model to be used in studying driver–vehicle interactions. Drivers interact with their vehicle and the road through the steering wheel. This interaction forms a closed-loop coupled human–machine system, which influences the driver's steering feel and control performance. A hierarchical approach is proposed here to capture the complexity of the driver's neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities, especially in the presence of external disturbance. The proposed motor control framework has three layers: the first (or the path planning) plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory; the second (or the musculoskeletal controller) actuates the musculoskeletal arm to rotate the steering wheel accordingly; and the final layer ensures the precision control and disturbance rejection of the motor control units. The physics-based driver model presented here can also provide insights into vehicle control in relaxed and tensed driving conditions, which are simulated by adjusting the driver model parameters such as cognition delay and muscle co-contraction dynamics.

<div class="htmlview paragraph">The authors have developed a steering entropy method to easily and accurately quantify the workload imposed on drivers who are engaged in activities apart from the normal driving operations of longitudinal and lateral control. A driver's steering behavior tends to become more discontinuous while performing an activity in addition to driving. To quantify these discontinuities, steering entropy values are obtained from a time-series history of steering angle data. A special-purpose driving simulator and a test procedure have been developed that allow workload evaluations to be conducted efficiently. The simulator and test procedure were used to evaluate the additional workload incurred by 14 different types of activities. The steering entropy results were compared with a dual task method as well as a subjective evaluation method. These results showed a high degree of correlation between the three approaches, which confirmed the validity of the steering entropy method as a means to quantify workload. Measurements were then made of progressively more difficult tasks. It was found that the steering entropy values increased in direct proportion to the degree of (subjective) task difficulty, which corroborated the high measurement accuracy of this method.</div>

Driver assistance systems and vehicle safety systems are meant to improve the driving performance of a driver. Despite increased efforts in educating public at large on the danger of impaired driving in recent years, the problem is not alleviated and drunk driving is still one of the major causes of fatal accidents. Although different models of sober drivers are available in literature, mathematical modeling of humans under the influence of alcohol / drugs in steering control of a vehicle is open. Using system identification techniques, four different linear models for these drivers are presented here. The parameters and characteristics of these models are compared with the respective models for sober drivers. Larger delay (reaction time), reduced ability of maintaining the car in the center of lane, and aggressive driving style are some of the important features of intoxicated drivers identified here. The proposed models are validated using the data collected from 50 minute driving sessions for both sober and drunk drivers. The parametric uncertainties of the model parameters are also shown.

<div class="section abstract"><div class="htmlview paragraph">Driver steering feature clustering aims to understand driver behavior and the decision-making process through the analysis of driver steering data. It seeks to comprehend various steering characteristics exhibited by drivers, providing valuable insights into road safety, driver assistance systems, and traffic management. The primary objective of this study is to thoroughly explore the practical applications of various clustering algorithms in processing driver steering data and to compare their performance and applicability. In this paper, principal component analysis was employed to reduce the dimension of the selected steering feature parameters. Subsequently, K-means, fuzzy C-means, the density-based spatial clustering algorithm, and other algorithms were used for clustering analysis, and finally, the Calinski-Harabasz index was employed to evaluate the clustering results. Furthermore, the driver steering features were categorized into lateral and longitudinal categories. Different clustering algorithms were selected for clustering analysis to assess potential differences between lateral and longitudinal steering features and to determine the applicability of various algorithms for this purpose. In the comparative analysis, we comprehensively evaluate the performance of various algorithms in terms of clustering quality, interpretability, computational efficiency, and applicability. Selecting algorithms suitable for specific tasks and datasets is crucial to enhancing computational efficiency and the quality of clustering results. Through the comparative analysis of the driver steering feature clustering algorithm, a deeper understanding of the steering behavior mode can be obtained, providing support for optimizing the driver assistance system, advancing automatic driving technology, enhancing the driving experience, optimizing traffic management, and ultimately realizing a more intelligent and safer road traffic system.</div></div>

Highway fatalities in Japan have been increasing over the past 3 years despite decline of traffic fatalities. Most of accidents on highway are caused by careless driving including drowsy driving. Therefore, a number of researches on detecting such drowsy driving have been carried out, and various detection methods have been proposed: steering behavior, heart rate, etc. Especially focusing on such drowsy driving detection, this paper proposes a model-based method evaluating driving behavior using driving data which can be measured with existing on-board vehicle sensors in terms of ease of implementation. The model-based method can eliminate influence of driving environment so it can potentially evaluate small change of driver state. According to the comparison between the actual driving data and the driver model, the error between them correlates with subjective driver drowsiness. As a result, this study exhibits the feasibility of the proposed method that it can detect the drowsy driving by using the driver models.

This paper describes a lateral disturbance compensation algorithm for an application to a motor-driven power steering (MDPS)-based driver assistant system. The lateral disturbance including wind force and lateral load transfer by bank angle reduces the driver's steering refinement and at the same time increases the possibility of an accident. A lateral disturbance compensation algorithm is designed to determine the motor overlay torque of an MDPS system for reducing the manoeuvreing effort of a human driver under lateral disturbance. Motor overlay torque for the compensation of driver's steering torque induced by the lateral disturbance consists of human torque feedback and feedforward torque. Vehicle–driver system dynamics have been investigated using a combined dynamic model which consists of a vehicle dynamic model, driver steering dynamic model and lateral disturbance model. The human torque feedback input has been designed via the investigation of the vehicle–driver system dynamics. Feedforward input torque is calculated to compensate additional tyre self-aligning torque from an estimated lateral disturbance. The proposed compensation algorithm has been implemented on a developed driver model which represents the driver's manoeuvreing characteristics under the lateral disturbance. The developed driver model has been validated with test data via a driving simulator in a crosswind condition. Human-in-the-loop simulations with a full-scale driving simulator on a virtual test track have been conducted to investigate the real-time performance of the proposed lateral disturbance compensation algorithm. It has been shown from simulation studies and human-in-the-loop simulation results that the driver's manoeuvreing effort and a lateral deviation of the vehicle under the lateral disturbance can be significantly reduced via the lateral disturbance compensation algorithm.

In this paper, a pattern recognition approach is developed to identify the driver steering behavior characteristics. The detailed process is divided into three parts, feature parameters extracting, clustering process and identification model building. The driving simulator experiments are designed to obtain the primitive data, and the feature parameters are extractedto profile each driver steering manipulation sample. After that,all the driver steering manipulation samples are clustered with the aid of K-means and Gaussian mixture model (GMM). Based on the samples and the corresponding cluster labels, two identification models of driver steering behavior characteristics are built with two typical pattern recognition methods respectively, which are BP Artificial neural network (BP_ANN) and Support vector machine (SVM). The result shows that the BP_ANN model has higheridentificationaccuracyand can realize the pattern recognition of driver’s driving habits.

Speed choice is strongly influenced by geometric road features. In this work, the influence of lane and shoulder widths on free-flow speed was studied with the driving simulator DriS at the University of Porto in Porto, Portugal. To evaluate how speed was influenced by the cross section, this study investigated the possible influence of the order of magnitude of the practiced speeds on the effects of variations in lane and shoulder widths. Two types of roads with different base speeds were considered. The roads were presented to drivers on a driving simulator. The validity of the data obtained in the simulator was confirmed through a comparative analysis of the registered speeds in the real environments for the equivalent simulator conditions at six points of control. The lane and shoulder widths from which the free-flow speed was no longer conditioned by the dimensions of the road's cross section were obtained, as well as the reduction in speed associated with smaller widths. Contrary to what was suggested by the Highway Capacity Manual 2010, the individual effects of variations in lane and shoulder widths were not cumulative; greater impacts on free-flow speed were obtained by their simultaneous variation.

SUMMARYSimulations of closed-loop manoeuvres where the driver is modelled in a vehicle-road environment axe of increasing importance in car development. To be able to investigate manoeuvres with large local deviations from a desired road trajectory the 2-level standard driver model, which is based on previewed steering, is extended by a 3rd level which initiates a smooth transition to the road trajectory. The corresponding control design is based on the two-wheel vehicle model and the sliding control technique, and more over it can be adapted to different steering intensities and driving situations. The simulation results are based on a complex 3D vehicle model and consider different tyre-road friction conditions. An obstacle avoidance manoeuvre and the entering of a curve (with clothoide) with a μ-low section in the circular part prove the quality of this 3-level approach.

Measuring and modeling driver steering behavior: From compensatory tracking to curve driving

With the increase in driver assistance systems, driver models are becoming more important to vehicle control, driving safety, and performance. To make these driver assistance systems better cooperate with human drivers, the driver models need to be able to predict human driving behaviors and distinguish among different drivers. In this paper, a combined driver model consisting of a compensatory transfer function and an anticipatory component based on road geometry is integrated with the design of the individual driver’s desired path. The proposed driver model parameters are obtained from human subject test data collected in a driving simulator. It has been shown that the proposed combined driver model is able to replicate each driver’s steering wheel angle signals for a variety of maneuvers at different vehicle speeds. The driver model is then validated by first using a polynomial to interpolate the driver model and desired path parameters for an intermediate speed. It is also validated by comparing two different drivers’ model parameter sets to show that each driver has a unique set of parameters. The final validation is to show that the proposed individual driver’s desired path offers more accurate steering wheel fits than the previous geometric centerline.

Due to rapid urbanization, a rise in vehicle growth, and a lack of adherence to traffic rules and regulations by drivers and pedestrians, traffic accidents involving pedestrians have become a significant safety problem in most developing countries, including Ethiopia. Urban areas account for the majority of pedestrian accidents. Traffic accidents involving pedestrians are more likely to occur when they are crossing the road. The majority of these accidents occur when pedestrians use rolling gap crossing behavior. The main objective of this study is to explore pedestrian rolling gap crossing behavior at intersection crosswalks in Gambella town. Data regarding pedestrian rolling gap crossing, pedestrian, geometric and environmental characteristics were collected through videography survey method, on-site observation, and measurement. An observational checklist was used to conduct on-site observations at each crosswalk at the selected intersection. Road width, lane width, and median width were also measured using meter tape. Using the collected data, a binary logistic regression model was developed. The developed model revealed that three statistically significant factors significantly affected pedestrian rolling gap crossing. These factors included gender, age, and crosswalk condition. Furthermore, secondary sources were reviewed, including ERA manual, AASHTO manual, and Gambella town master plan. The results of on-site observation, measurements, and the review of secondary sources were used to suggest remedial measures at existing intersection crosswalks.

<div class="section abstract"><div class="htmlview paragraph">Driver model has been developed since 1950s and quite a lot of attention has been attracted on this aspect. Today there is a body of knowledge regarding driver mathematic model. Since driving behavior is highly complex, involving psychology and physiology factors, it is not easy to use a simple driver model to represent all characteristic features comprehensively, according to the utilization of driver model, some assumptions are necessarily proposed in order to tackle the specific problem easily.</div><div class="htmlview paragraph">Optimal Preview Acceleration Driver Model was put forward based on the Preview-Following Theory proposed by Prof. Guo in 1983. This driver model is quite simple and easy to understand, and accurate for great curvature path following. However, recent study has shown that more work is needed to improve the path following ability for sharp corner or urgent corner (higher frequency steering input). This paper will investigate and analyze the Optimal Preview Acceleration Driver Model in details, and propose an improvement based on this classical driver model to make it suitable for more specific conditions, especially for urgent cornering. Finally, the modified driver model will be verified to be more capable of realizing high frequency steering behavior than the previous driver model.</div></div>