Modeling Human Steering Behavior During Path Following in Teleoperation of Unmanned Ground Vehicles

Mirinejad, Hossein; Jayakumar, Paramsothy; Ersal, Tulga

doi:10.1177/0018720818769260

Cited by 7 publications

(24 citation statements)

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“…If the cognitive models can be extended to shared control modes, in which teleoperators share the control authority with onboard autonomy, then the models can benefit the development and evaluation of semiautonomous solutions, as well. Mirinejad et al (2018) have shown that speed can have a strong impact on the ALKE performance in constant speed settings. In this work, Lower tailed hypothesis at 2 σ .005…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have shown that human operation behavior varies under different driving conditions for teleoperation, such as different communication delays and vehicle speeds (Mirinejad et al, 2018). Similarly, a set of control gains that is able to describe the human operator behavior under a certain teleoperation condition may not capture the human operator behavior under a different condition.…”

Section: Model Tuningmentioning

confidence: 99%

“…As shown by Mirinejad et al (2018), the optimal model parameters vary as vehicle speed changes. Parameters that work well at low turning speed could become too aggressive at higher turning speeds and are susceptible to small variations in the control parameters.…”

Section: Model Tuningmentioning

confidence: 99%

“…The simulation platform used in this study is similar to the one used in Mirinejad et al (2018), which is a fixed-based teleoperated driving simulator developed by Zheng et al (2016). It was loaded with a 14 degrees-of-freedom vehicle dynamics model that is representative of a notional medium-duty military truck (Liu et al, 2016).…”

Section: Equipmentmentioning

confidence: 99%

“…However, the dynamics of a small ground robot and its user interface can be dramatically different compared to passenger-sized UGVs. To the best of the authors’ knowledge, the only teleoperator model for a passenger-sized UGV is the work of Mirinejad et al (2018), where the authors applied a cognitive framework, namely the Adaptive Control of Thought-Rational (ACT-R), to model the human steering behavior for a passenger-sized UGV that operates under a constant speed. Their results demonstrate the feasibility of modeling operator behavior in the cognitive framework while capturing the effect of time delay and nonnegligible vehicle dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

Tang

Zheng

et al. 2020

Hum Factors

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Objective This paper extends a prior human operator model to capture human steering performance in the teleoperation of unmanned ground vehicles (UGVs) in path-following scenarios with varying speed. Background A prior study presented a human operator model to predict human steering performance in the teleoperation of a passenger-sized UGV at constant speeds. To enable applications to varying speed scenarios, the model needs to be extended to incorporate speed control and be able to predict human performance under the effect of accelerations/decelerations and various time delays induced by the teleoperation setting. A strategy is also needed to parameterize the model without human subject data for a truly predictive capability. Method This paper adopts the ACT-R cognitive architecture and two-point steering model used in the previous work, and extends the model by incorporating a far-point speed control model to allow for varying speed. A parameterization strategy is proposed to find a robust set of parameters for each time delay to maximize steering performance. Human subject experiments are conducted to validate the model. Results Results show that the parameterized model can predict both the trend of average lane keeping error and its lowest value for human subjects under different time delays. Conclusions The proposed model successfully extends the prior computational model to predict human steering behavior in a teleoperated UGV with varying speed. Application This computational model can be used to substitute for human operators in the process of development and testing of teleoperated UGV technologies and allows fully simulation-based development and studies.

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Section: Discussionmentioning

confidence: 99%

Section: Model Tuningmentioning

confidence: 99%

Section: Model Tuningmentioning

confidence: 99%

Section: Equipmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

Tang

Zheng

et al. 2020

Hum Factors

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Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Negash

Yang

2023

IEEE Access

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Driver behavior models have been used as input to self-coaching, accident prevention studies, and developing driver-assisting systems. In recent years, driver behavior recognition has revolutionized autonomous vehicles (AVs) and traffic management studies. This comprehensive survey provides an up-todate review of the different driver behavior models and modeling approaches. In heterogeneous streets where humans and autonomous vehicles operate simultaneously, predicting the intent and action of human drivers is crucial for AVs with the help of wireless communication and artificial intelligence (AI) technologies. Therefore, the review also summarizes the applications of driver behavior modeling (DBM) for effective behavior recognition and human-like AV driving. Moreover, the review also covers the application of DBM in capturing behaviors of complex dynamic driving tasks. In this review, we solely cover car-following (CF) and lane-changing (LC) maneuvers.INDEX TERMS Driver behavior modeling (DBM), autonomous vehicles, behavior recognition, human-like, car-following, lane-changing, perception, decision-making, data-driven, vehicle control. I. INTRODUCTIONIn 2018, the US Department of Transportation reported 6,734,000 motor vehicle-related accidents. 1 Among these accidents, there were 2.71 million injuries and 36,560 fatalities. These deaths and injuries also increased in the years 2019 and 2020. Earlier in 2015, specifically in the US, 80 − 94% of crashes were due to driver-related errors, and 33% were due to the wrong prediction of other drivers [1]. The statistics of accidents caused by human driver-related errors were also reported in [2] and [3].The accidents and their behind reasons will lead us back to human drivers with rich and diverse behaviors. According to Michon [4], a human as a problem solver can be a road user, a transportation consumer, a social agent, and a psycho-biological organism. The issues to be solved are controlling a vehicle, trip-making, communication, and satisfying the basic driving necessity. A human driver can have unique traits towards vehicle controlThe associate editor coordinating the review of this manuscript and approving it for publication was Divanilson Rodrigo Campelo .

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Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

Tang,

Yan,

Wang

2023

IEEE Access

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By improving the ability of Trajectory Tracking Control (TTC) algorithms to mimic the manipulation behaviors of real drivers, which is of great significance in improving the personalized driving experience of autonomous vehicles. In this paper, we propose a TTC method that combines the Adaptive Control of Thought-Rational (ACT-R) cognitive theory framework with the Preview Tracking (PT) theory. Firstly, by analyzing and describing the ACT-R cognitive framework and the PT theory, a TTC framework that combines the two theories is proposed. Secondly, a virtual driving simulator was built to collect driving data from 30 drivers and construct a driving memory database. Thirdly, a central production system for driver trajectory tracking is designed, which consists of: three control modes, production rules, a timing generator, and filtering methods for driving memory segments. Fourthly, the TTC method based on PT theory is designed to adapt to different control modes. Finally, statistical and comparative analyses of the human-like trajectory tracking results of the proposed method were carried out through co-simulation experiments, and it was verified that the human-like performance of the proposed new method had a high degree of similarity with the manipulation behaviors and the vehicle motion state of the real driver. The real-vehicle experiments are carried out, which verifies the consistency of the proposed method with the results of the simulation experiments.INDEX TERMS Trajectory tracking control; human-like driving; autonomous vehicles; ACT-R cognitive framework; preview tracking theory.

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Modeling Human Steering Behavior During Path Following in Teleoperation of Unmanned Ground Vehicles

Abstract: The established model is a suited candidate to be used in place of human drivers for simulation-based studies of UGV mobility in teleoperation systems.

Cited by 7 publications

References 24 publications

Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

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