From vehicle stability control to intelligent personal minder: Real-time vehicle handling limit warning and driver style characterization

Lǚ, Jianbo; Filev, Dimitar; Prakah-Asante, Kwaku O.; Tseng, Fling; Kolmanovsky, Ilya

doi:10.1109/civvs.2009.4938722

Cited by 39 publications

(24 citation statements)

References 22 publications

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“…It is important to keep in mind that that vehicle stability control research is no longer constrained to preserving stability at all costs, and personalization trends are making its way into the vehicle stability domain as well; see, e.g., fuzzy logic quantification of the driver type category proposed in [13].…”

Section: Onboard Applicationsmentioning

confidence: 99%

Overview of CI research in automotive ITS

Prokhorov¹

2011

2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings

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Illustrative examples of applications of the CI methods in ITS are overviewed. They include off-board and on-board applications. On-board object recognition is further discussed from the standpoint of making a more reliable system for ITS applications. I. INTRODUCTIONComputational intelligence (CI) traditionally includes neural networks (NN), fuzzy logic systems (FS) and evolutionary computations (EC) as three main areas distinguishing CI in terms of employed methods from classical AI. The difference between CI and AI is blurred because both the IEEE Computational Intelligence Society and the Association for the Advancement of Artificial Intelligence have sometimes been employing essentially the same methods to attack same challenges.Instead of categorizing by methods, the division by application is accepted which is more useful for applied research. In this paper ITS applications of CI are separated in two large categories:1) On-board: inward (driver or vehicle) focused and outward (environment) focused, 2) Off-board: infrastructure based including communications and Vehicle-Infrastructure Integration (VII).The on-board applications are exemplified in [1], [2], which include but are not limited to driver behavior assessment (drowsiness, intentions, etc.), object recognition, vehicle health monitoring and intelligent control.The infrastructure applications are exemplified in [3], [4], [5], which include but are not limited to remote counting of road objects, recognizing license plates and other vehicle properties.In this paper traditional CI applications in ITS still in the research phase are illustrated. The state of art in object recognition is then summarized. It is also discussed what combining multiple sensors, or sensor fusion, could bring to the table and what perhaps is not so important for the ITS area. This paper is organized as follows. The next section briefly overviews illustrative off-board applications of CI in ITS and system architectures. Section III briefly overviews on-board applications. Section IV discusses in more details object recognition applications and their specifics for ITS, as well as overviews some experimental results, followed by Conclusion.

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Section: Onboard Applicationsmentioning

confidence: 99%

Overview of CI research in automotive ITS

Prokhorov¹

2011

2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings

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“…Older or improperly maintained vehicles may also result in more accidents. Onboard driving style identification [27], [28] and vehicle condition monitoring [29], [30] are becoming available. In this paper, we assume compounding correction factors of 1.2 and 1.1 for an aggressive driver and a poorly maintained vehicle, respectively.…”

Section: Driving Style and Vehicle Conditionsmentioning

confidence: 99%

Road Risk Modeling and Cloud-Aided Safety-Based Route Planning

Kolmanovsky

Atkins

et al. 2016

IEEE Trans. Cybern.

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This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the "best" route can be adjusted to favor time versus safety metrics.

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“…Besides, an additional torque eq is applied in yaw moment that results from the difference of the tire forces. Ignoring the air drag and tire rolling resistance, the vehicle dynamic equations,including longitudinal, lateral, and yaw motions, are given in (1) to (3):…”

Section: Vehicle Dynamics Modelsmentioning

confidence: 99%

“…Towards the developing trend of intelligent vehicle, ADAS (advanced driver assistant system) and active safety technology have drawn attention in recent years. Like Ford motor, it proposed a vehicle handling limit warning system based on VSC (vehicle stability control) for the next generation of accident-free vehicle [1]. Currently, the intelligent control system has been designed to handle transient unstable vehicle motion in a guaranteed-safety range.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal/Lateral Stability Analysis of Vehicle Motion in the Nonlinear Region

Chen

Pei

et al. 2016

Mathematical Problems in Engineering

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We focus on the study of motion stability of vehicle nonlinear dynamics. The dynamic model combining with Burckhardt tire model is firstly derived. By phase portrait method, the vehicle stability differences of three cases, front wheels steering/four-wheel steering case, front/rear/four-wheel braking case, and high/low road friction case, are characterized. With the Jacobian matrix, the stable equilibrium point is found and stable areas are calculated out. Similarly, the stability boundaries corresponding to different working conditions are also captured. With vehicle braking or accelerating in the steering process, the relationship between front/rear wheel slippage and the stable area is examined. Comparing with current literatures, the research method and its results present the novelty and provide a guideline for new vehicle controller design.

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From vehicle stability control to intelligent personal minder: Real-time vehicle handling limit warning and driver style characterization

Cited by 39 publications

References 22 publications

Overview of CI research in automotive ITS

Overview of CI research in automotive ITS

Road Risk Modeling and Cloud-Aided Safety-Based Route Planning

Longitudinal/Lateral Stability Analysis of Vehicle Motion in the Nonlinear Region

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