Individual Driver Modeling via Optimal Selection of Steering Primitives

Flad, Michael; Trautmann, Clemens; Diehm, Gunter; Hohmann, Soeren

doi:10.3182/20140824-6-za-1003.00223

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…Other approaches include the use of a driver model based on movement primitives (movemes) [115], [116] and a variable preview-time model based on road curvature. Additionally, it is common to model the driver as a linear quadratic problem with a path-tracking optimization function [17].…”

Section: B Model-based Coupled Shared Controllersmentioning

confidence: 99%

A Review of Shared Control for Automated Vehicles: Theory and Applications

Marcano

Díaz

Pérez

et al. 2020

IEEE Trans. Human-Mach. Syst.

156

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The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory-and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driverin-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.

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Section: B Model-based Coupled Shared Controllersmentioning

confidence: 99%

A Review of Shared Control for Automated Vehicles: Theory and Applications

Marcano

Díaz

Pérez

et al. 2020

IEEE Trans. Human-Mach. Syst.

156

View full text Add to dashboard Cite

show abstract

“…2 In order to set up a model for the driver's swerving behaviors, scholars at home and abroad propose various models based on different hypothesis and theories such as optimal curvature, 3 optimal preview, 4 multi-point preview, 5 and other driver models. 6,7 At present, the more common driver model is the driver model based on ''preview following theory'' proposed by Academician Guo. 8 Although these models can effectively present features of drivers and better realize the objective evaluation and swerving control of vehicles, they do not take real steering habits and mechanic features into consideration, which makes it difficult to come close to the operational level of human drivers.…”

Section: Introductionmentioning

confidence: 99%

Model predictive driver model considering the steering characteristics of the skilled drivers

Jiang

Tian

Hua

2019

Advances in Mechanical Engineering

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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 b-spline curve.

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“…A significant benefit of the movemes is that they can comparatively easy be identified using the algorithms submitted in [13] [14] and yield guarantees regarding correct identification. In previous work [15] [16] we proposed a driver model for the steering task which utilizes these movemes to generate a trajectory of driver steering motion. Thereby a switching controller determines the moveme sequence the driver applies to control the vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…It is shown that the model can adequately describe the driving behavior [16]. However, due to the MPC framework the use of the model in a practical ADAS application is difficult, since the calculation time in a real-time environment is very high [15]. In addition, the driver model yields a deterministic prediction of the driver steering actions which does not allow any further interpretation.…”

Section: Introductionmentioning

confidence: 99%

Gray-Box Driver Modeling and Prediction: Benefits of Steering Primitives

Inga

Flad

Diehm

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Shared control is a promising approach for designing an Advanced Driver Assistance System, since it unifies the advantages of both manual control and full automation. However, for a true cooperative shared control ADAS the automation has to understand the human and thus a suitable model which describes the driver in the control loop is essential. Our gray-box approach bases on the biological concept that humans realize motion by combining a finite set of motion primitives (we call movemes). With the assumption that a driver switches between movemes based on perceived information, we propose a Hidden Markov Model which determines the probability of each moveme given a certain driving situation. Car turn maneuver experiments show a good approximation of steering trajectories recorded in a driving simulator. A comparison with a black-box model show that the moveme-based driver model performs significantly better. In addition, training algorithms are available and the probabilistic approach of the model allows further interpretation of the results.

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Individual Driver Modeling via Optimal Selection of Steering Primitives

Cited by 12 publications

References 25 publications

A Review of Shared Control for Automated Vehicles: Theory and Applications

A Review of Shared Control for Automated Vehicles: Theory and Applications

Model predictive driver model considering the steering characteristics of the skilled drivers

Gray-Box Driver Modeling and Prediction: Benefits of Steering Primitives

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