Model predictive assisting control of vehicle following task based on driver model

Mikami, Koji; Okuda, Hiroyuki; Taguchi, Shun; Tazaki, Yuichi; Suzuki, Tatsuya

doi:10.1109/cca.2010.5611209

Cited by 15 publications

(15 citation statements)

References 14 publications

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“…A Probability-weighted ARX model (PrARX model, [10], [11]) combines multiple ARX models using a probabilistic weighting function of the following form:…”

Section: Prarx Modelmentioning

confidence: 99%

“…Personalization has been addressed in some conventional research [5], [8], [9]. These technologies enable the design This work was partly supported by JST CREST Japan.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, to realize safe and human-friendly vehicle, various types of Advanced Driver Assistance Systems (ADAS) such as adaptive cruise control (ACC) and lane keeping assistance system (LKAS) have been developed and implemented [1]- [5]. However, because of the requirement to accommodate to a wide variety of drivers, the developed systems tend to be conservative.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, when cooperative assistance is considered, assistance inputs for multiple vehicles must be optimized simultaneously, which increases computational cost much further. This paper presents a novel driver assisting system that combines driver models expressed as Probability Weighted ARX (PrARX) models [10], [11] and a fast computation method for nonlinear MPC based on continuation. The main purpose of the proposed system is an assistance for the driver but mostly not an autonoumous control.…”

Section: Introductionmentioning

confidence: 99%

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Model predictive driver assistance control for cooperative cruise based on hybrid system driver model

Okuda

Guo

Tazaki

et al. 2014

2014 American Control Conference

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This paper presents a driver assisting system for coorperative cruising of multiple cars. In order to account for personal difference of individual drivers, each driver's vehicle following skill on expressways is identified as a PrARX model, a continuous approximation of hybrid dynamical system. The PrARX model describes the driver's logical decision making as well as continuous maneuvering in a uniform manner. The assisting acceleration is computed in the framework of model predictive control, where the plant model is a platoon of cars coupled with PrARX driver models. For computing assisting outputs in real time, a fast computation method for nonlinear model predictive control based on the continuation technique is employed. The proposed assisting system is tested in numerical simulations and on a driving simulator with a real human driver. The high speed calculation of the Homotopy method is also proved by comparison to the conventional method. Finally, the advantage of global optimization for cooperative safety is confirmed by comparing its control performance with the local optimization for individual safety.

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“…A Probability-weighted ARX model (PrARX model, [10], [11]) combines multiple ARX models using a probabilistic weighting function of the following form:…”

Section: Prarx Modelmentioning

confidence: 99%

“…Personalization has been addressed in some conventional research [5], [8], [9]. These technologies enable the design This work was partly supported by JST CREST Japan.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model predictive driver assistance control for cooperative cruise based on hybrid system driver model

Okuda

Guo

Tazaki

et al. 2014

2014 American Control Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…Simulation was used to validate the control performance compared with the optimal preview driver model proposed before. Mikami et al developed a personalised driver assisting system, which made use of a driver behaviour model [13]. They produced the Probability-weighted ARX (PrARX) model, as a driving behaviour model, to describe the driver's vehiclefollowing skill.…”

Section: Introductionmentioning

confidence: 99%

Modeling a Driver’s Directional and Longitudinal Speed Control Based on Racing Track Features

Wang

et al. 2018

Shock and Vibration

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This study firstly analyses the driver's manipulation behaviour and relates the different components of the driver model. Then, a model controlling the driver directions is built according to the prediction-follower theory with the aim of improving the point search algorithm. A model of the driving system of an electric vehicle is used to establish the longitudinal speed control model of the driver by using a feedforward-PID feedback control strategy. Our approach is to release the coupling between direction and speed control and build an integrated model that includes the direction and speed for an arbitrary path. Finally, the characteristics of an actual racing track are considered to establish the fastest driver control model. We simulated the typical operating conditions of our driver operation model. The simulation confirmed the effectiveness of the improved predictive point search algorithm and the integrated driver model to control the direction and speed for an arbitrary path.

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Personalized Speed Planning Algorithm Using a Statistical Driver Model in Car-following Situations

Baek

Kim²,

Han

2022

Int.J Automot. Technol.

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Model predictive assisting control of vehicle following task based on driver model

Cited by 15 publications

References 14 publications

Model predictive driver assistance control for cooperative cruise based on hybrid system driver model

Model predictive driver assistance control for cooperative cruise based on hybrid system driver model

Modeling a Driver’s Directional and Longitudinal Speed Control Based on Racing Track Features

Personalized Speed Planning Algorithm Using a Statistical Driver Model in Car-following Situations

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