Lateral load transfer and normal forces estimation for vehicle safety: experimental test

Doumiati, Moustapha; Victorino, Alessandro Corrêa; Charara, Ali; Lechner, Daniël

doi:10.1080/00423110802673091

Cited by 83 publications

(34 citation statements)

References 11 publications

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“…I shows that the kinetic tire friction coefficient is typically slightly lower than the static stability factor /2h (the average static stability factor among vehicle models introduced in 2003 was approximately 1.4 for passenger cars and 1.2 for SUVs [28]), and hence, the no-sliding condition should be violated before the no-rollover condition. That being said, a more realistic model including e.g., normal forces or friction forces unevenly distributed among the contact points between the tires and the road surface [29], [30], nonzero pitch or roll angles [26], or tripped rollovers [31], would complicate the analysis. Now, we will identify cornering events by observing whether either of the statistics in (2) and (5) reach some predetermined thresholds, that is, whether the driver violates the condition C : T (v, a, ω) < γ SL and T (v, 0, ω) < γ RO (6) where γ SL = c 1 · µ and γ RO = c 2 · /2h for some constants c 1 , c 2 ∈ (0, ∞).…”

Section: Problem Formulationmentioning

confidence: 99%

Risk assessment of vehicle cornering events in GNSS data driven insurance telematics

Wahlström

Skog

Händel

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-We propose a framework for the detection of dangerous vehicle cornering events, based on statistics related to the no-sliding and no-rollover conditions. The input variables are estimated using an unscented Kalman filter applied to global navigation satellite system (GNSS) measurements of position, speed, and bearing. The resulting test statistic is evaluated in a field study where three smartphones are used as measurement probes. A general framework for performance evaluation and estimator calibration is presented as depending on a generic loss function. Further, we introduce loss functions designed for applications aiming to either minimize the number of missed detections and false alarms, or to estimate the risk level in each cornering event. Finally, performance characteristics of the estimator is presented as depending on the detection threshold, and on design parameters describing the driving behavior. Since the estimation only uses GNSS measurements, the framework is particularly well-suited for smartphone-based insurance telematics applications, aiming to avoid the logistic and monetary costs associated with e.g., on-board-diagnostics or black-box dependent solutions. The design of the estimation algorithm allows for instant feedback to be given to the driver, and hence, supports the inclusion of real time value added services in usage-basedinsurance programs.

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Section: Problem Formulationmentioning

confidence: 99%

Risk assessment of vehicle cornering events in GNSS data driven insurance telematics

Wahlström

Skog

Händel

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…14 In order to better represent the vehicle lateral and yaw dynamics as well as coupling of yaw-roll motion due to the transient lateral load transfer 18 during extreme maneuvers, a 9 DOF model is valid for applications which do not involve wheel lift-off.…”

Section: Vehicle Dynamic Modelmentioning

confidence: 99%

Evaluating the tire wear quantity and differences based on vehicle and road coupling method

Chen

et al. 2017

Advances in Mechanical Engineering

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Tire wear quantity and difference is one of the main points that affect the intension of tire road print feature and is under a tight link not only with tire road friction energy but also with vehicle dynamics. The proposed methodology aims to provide a mathematical method to analyze tire marks' feature for prediction the vehicle operating status in pro-and post-crash phases. The kinetic sliding friction coefficient considering the road effect is simplified with the new real contact area model. After, a simplified 9 degrees of freedom vehicle dynamics model by coupling the tire dynamic model is set up, and the effectiveness of the model is evaluated with vehicle stability measurement equipment. Furthermore, a theoretical model of tire wear quantity and difference as a function of road properties and vehicle dynamics is set up combined with vehicle-road coupling algorithm, and important parameters (e.g. vehicle speed, steering angle, braking force, and road feature) are analyzed to assess the tire wear quantity and difference feature when the vehicle is in unsteady condition. Results show that vehicle speed, braking force, and road surface feature have a significant impact to tire wear quantity and difference, the right rear exhibits a fluctuation and the tire wear quantity and difference between rear axle and the right side is obvious, and lay a strong foundation for tire road print analysis.

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“…In order to estimate μ max , many parameters should be observed such as side slip angle, normal and lateral forces acting on tyre, velocity of the vehicle, etc. [22][23][24][25][26][27][28]. Velocity of the vehicle could be observed from speedometer, rear wheel angle sensors or GPS.…”

Section: Introductionmentioning

confidence: 99%

Collision Avoidance via Adaptive Trajectory Control in Case of a Sudden Decrease in the Maximum Road Friction Coefficient

Şahin

2017

PROMET

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Lateral load transfer and normal forces estimation for vehicle safety: experimental test

Cited by 83 publications

References 11 publications

Risk assessment of vehicle cornering events in GNSS data driven insurance telematics

Risk assessment of vehicle cornering events in GNSS data driven insurance telematics

Evaluating the tire wear quantity and differences based on vehicle and road coupling method

Collision Avoidance via Adaptive Trajectory Control in Case of a Sudden Decrease in the Maximum Road Friction Coefficient

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