CAPC: A Road-Departure Prevention System

LeBlanc, David J.; Johnson, Glen E.; Venhovens, Paul; Gerber, G.; DeSonia, R.; Ervin, R D; Lin, Chiu-Feng; Ulsoy, A. Galip; Pilutti, Tom E.

doi:10.1109/37.546271

Cited by 83 publications

(26 citation statements)

References 8 publications

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“…Regarding this dimensionless model and the previous discussion on dimensionless parameters, nearly all the Pi parameters are constant and approximately given by the average measurements reported in Equation (18). The exception is the p 3 parameter, which remains as the only varying, unknown parameter due to the time-varying vehicle velocity or cornering stiffness.…”

Section: Development Of the Identification Modelmentioning

confidence: 93%

“…The derivation use of the bicycle model was historically explained in detail by Dugoff et al [17]. Although the bicycle model is relatively simple, many investigations have verified that it remains a good approximation for full-size vehicle dynamics as long as accelerations are limited to 0:3g's [18]. With regard to the underlying model, the bicycle model does not account for non-linear tire dynamics, and the only knowledge of the tire-road interface is represented by the front and rear cornering stiffness parameters, C af and C ar : The cornering stiffness represents the slope near the origin of the curve of the lateral tire forces as the dependent variable and the sliding angle of the tire with respect to the road, a.k.a.…”

Section: Vehicle Dynamicsmentioning

confidence: 99%

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Real‐time identification of vehicle chassis dynamics using a novel reparameterization based on sensitivity invariance

Brennan

Alleyne

2004

Adaptive Control & Signal

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This work presents a novel methodology to identify model parameters using the concept of sensitivity invariance. Within many classical system representations, relationships between Bode parameter sensitivities may exist that are not explicitly accounted for by the formal system model. These relationships, called sensitivity invariances, will explicitly limit the possible parameter variation of the system model to a small subspace of the possible parameter gradients. By constraining the parameter identification or adaptation to a model structure with uncoupled parameter sensitivities, a more efficient identification can be obtained at a reduced computational and modelling cost. As illustration, an identification method of using sensitivity invariance is demonstrated on an experimental problem to identify, in real time, a timevarying tire parameter associated with the chassis dynamics of passenger vehicles at highway speeds. The results are validated with simulations as well as an experimental implementation on a research vehicle driven under changing road conditions.

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Section: Development Of the Identification Modelmentioning

confidence: 93%

Section: Vehicle Dynamicsmentioning

confidence: 99%

Real‐time identification of vehicle chassis dynamics using a novel reparameterization based on sensitivity invariance

Brennan

Alleyne

2004

Adaptive Control & Signal

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“…Similar to V2V, vehicular application of CS technology is evident with car manufacturers such as Ford, General Motors, Daimler, and BMW [43]- [46]. For more information regarding CS technology, please refer to Section 4.5 Camera Systems.…”

Section: Cs Camera Systems Technologymentioning

confidence: 99%

“…as well as traffic signs [45], [73], [74]. CS's are commonly deployed to enhance safety as a Forward Collision Warning (FCW) systems (where the system objective is to enhance driving safety) and 42 as Lane Departure Warning (LDW) systems (where the CS tracks vehicle position in surrounding lanes and alerts the driver if crossing lane boundary without signaling) [43].…”

Section: Cs Overviewmentioning

confidence: 99%

A Planning Tool to Assess Advanced Vehicle Sensor Technologies on Traffic Flow, Fuel Economy, and Emissions

Wifvat

2018

SAE Technical Paper Series

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“…The prediction is used to compute Time of Lane Crossing (TLC) for automated driver assistance on highways; the system employed a camera to sense roadway in front of the vehicle, measured front wheel steering angle and employed a gyroscope to measure the yaw rate [8]. In this work, path prediction is achieved by numerically integrating a simplified linear "bicycle" model of the vehicle's lateral motion using state estimates from the near-range Kalman filter, as well as the vehicle's initial conditions [9], rather than the vehicle's kinemtic-dynamic model. The prediction errors were statistically characterized in [10].…”

Section: Model Validation Resultsmentioning

confidence: 99%

A physics-based model for predicting user intent in shared-control pedestrian mobility aids

Wasson

Sheth²,

Huang³

et al.

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)

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Abstract-This paper presents a physics-based model approach to infer navigational intent of the user of a walker, based on measuring forces and moments applied to the walker's handles. Our experiments use two 6-DOF force/moment sensors on the walker's handles, a 2-D kinematic-dynamic model of the walker and a digital motion capture system to trace the path of the walker. The motion capture system records the path the walker follows while the 6 DOF sensors record the handle forces used to guide the walker along that path. A dynamic model of the walker that determines user navigational intent from force/moment data was developed and validated against the motion capture data streams. This paper describes the development and validation of the model as well as plans for using the model as a path predictor. The inferred user intent will be incorporated into a passive shared steering control system for the walker.

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CAPC: A Road-Departure Prevention System

Cited by 83 publications

References 8 publications

Real‐time identification of vehicle chassis dynamics using a novel reparameterization based on sensitivity invariance

Real‐time identification of vehicle chassis dynamics using a novel reparameterization based on sensitivity invariance

A Planning Tool to Assess Advanced Vehicle Sensor Technologies on Traffic Flow, Fuel Economy, and Emissions

A physics-based model for predicting user intent in shared-control pedestrian mobility aids

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