Analysis of Vehicle Following Behavior of Human Driver Based on Hybrid Dynamical System Model

Akita,; Inagaki,; Сузуки,; Hayakawa,; Tsuchida,

doi:10.1109/isic.2007.4359705

Cited by 10 publications

(13 citation statements)

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“…In the peg‐in‐hole task and grasping tasks in which the status of contact with the environment varies over time, unconscious mode transitions caused by changes in the environment appear to be present 8. Furthermore, when switching from low‐speed walking to high‐speed walking, or in obstacle avoidance actions or driving a vehicle or other operational actions, people appear to intentionally switch modes based on the state of completion of the target task 5–7. Also, even when a problem (failure) occurs in part of the body, the necessary mode transition to recover from it appears to occur.…”

Section: Hybrid Systems and Advanced Motionmentioning

confidence: 99%

“…This section discusses the application of a deterministic hybrid system model to the vehicle tracking behavior of a driver on a high‐speed road, as an example of advanced human behavior that involves perception and decision‐making 7. There has been considerable prior research 23–26 on the modeling of driver behavior.…”

Section: Modeling Vehicle Following Behavior By a Deterministic Hymentioning

confidence: 99%

“…We see that not only can the features of the four modes be glimpsed, but the conditions of the transitions between modes can also be derived by finding the segmentation planes between modes by means of an SVM. The details of the parameters obtained are omitted here, but it is clear that based on the results of an investigation of the parameters appearing in the segmentation planes between modes, the perceptual information that fills the most fundamental role in the transition between modes is KdB 7. Hence, modeling vehicle traffic behavior as a hybrid system is possible.…”

Section: Modeling Vehicle Following Behavior By a Deterministic Hymentioning

confidence: 99%

“…1) based on the idea that the advanced motion seen in humans is achieved by a series of primitive acts and switching (decision‐making) among them; we then discuss its utility and future possibilities. In particular, this paper considers vehicle following behavior 7 and the peg‐in‐hole task 8 as specific examples of advanced human behavior and high‐level skill, and demonstrates the effectiveness of treating them as hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

“…In view of the above, in this paper we propose a new framework 2–9 to analyze and design advanced motion from the perspective of a continuous/discrete hybrid system (for instance, Ref. 1) based on the idea that the advanced motion seen in humans is achieved by a series of primitive acts and switching (decision‐making) among them; we then discuss its utility and future possibilities.…”

Section: Introductionmentioning

confidence: 99%

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Advanced motion as a hybrid system

Suzuki

2010

Elect Comm in Japan

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SUMMARYThis paper presents a modeling and analysis strategy for advanced complex human motion based on a hybrid system framework. The motion modeling problem is addressed as a parameter estimation problem under the piecewise ARX model, which is a class of hybrid dynamical system. The advantages of motion modeling based on hybrid dynamics are: (1) complex dynamical behavior can be represented in terms of dynamical primitives together with the mechanism of switching between them; (2) the resulting model may be useful for various applications, such as the design of assist controllers, recognition of human behavior, and so on. Finally, the newly developed strategy is applied to a driver's vehicle following task and a peg-in-hole task, which represent two typical classes of complex human motion. The results obtained show the usefulness of hybrid dynamics for representing complex human motion.

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Section: Hybrid Systems and Advanced Motionmentioning

confidence: 99%

Section: Modeling Vehicle Following Behavior By a Deterministic Hymentioning

confidence: 99%

Section: Modeling Vehicle Following Behavior By a Deterministic Hymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced motion as a hybrid system

Suzuki

2010

Elect Comm in Japan

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Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

Nwadiuto

Yoshino²,

Okuda

et al. 2021

IEEE Access

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This paper investigates the decision-making characteristics of the driver in the overtaking on the highway road. For the research purpose, a novel method was proposed by introducing a logistic regression model accompanied by the statistical test technique, which does not require prior knowledge about the explanatory variables. This study hypothesizes that the driver's gazing behavior is crucial for the decision-making process in driving and hence, the line-of-sight information was introduced to estimate driver's gazing behavior in the model of driver's decision specifically for reproducing the overtaking driving behavior accurately. Consequently, the proposed model realized a high describability on the decision of the driver when performing the overtaking driving task, which is one of the significant advancements of the present study with respect to the past similar studies. This study integrates the perspectives of intelligent vehicle design and cognitive science by revealing which factor the driver pays attention to in a changeable driving environment due to various observable factors. In experiments based on the driving simulator with six human subjects, the overtaking behavior was successfully estimated by specifying a set of variables to reconstruct the driver's behavior and then the proposed model provided a minimum set of necessary variables accompanied with key coefficients. In conclusion, the proposed approach based on a simple logistic regression model demonstrated driving behaviors with an accurate estimation of the driver's intention without the need for prior knowledge, and it may contribute to higher describability for various driving actions in a dynamic environment.

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