Analyses of Vision-based Lateral Control for Automated Highway System

Hsu, Jin-Chuan; Tomizuka, Masayoshi

doi:10.1080/00423119808969456

Cited by 18 publications

(9 citation statements)

References 6 publications

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“…where the notations F i, j represent the x or y components of the lateral force vectors in Eqn. (11). The coefficients of the virtual pseudo-velocities in Eqn.…”

Section: Dynamic Vehicle Modelmentioning

confidence: 99%

“…The corresponding image processing algorithms are generally rather resource intensive, and most commercialized solutions are not capable of processing road information with high frequency [8,9]. As an example, the vision delay was considered to amount to 0.15 s in [10] and 0.2 s in [11]. In [12], a time constant of 0.3 s was used for a low level steering angle controller, while [3] used 0.4 s as a conservative estimate for the steering delay.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lane Keeping Control Using Finite Spectrum Assignment With Modeling Errors

Vörös

Várszegi

Takács³

2019

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive

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Lane keeping control of the single track vehicle model with linear tire characteristics is analyzed in the presence of time delay. In order to compensate time delay, the predictor control approach called finite spectrum assignment is applied. This controller uses an internal model of the plant to predict current system states in spite of the time delay. The predictions are based on a simplified version of the vehicle model, neglecting tire dynamics. The predictive control approach is compared with traditional feedback control using analytically derived stability maps and numerical simulations. Robustness to parameter mismatches and numerical issues related to the implementation of the control law are also analyzed.

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“…where the notations F i, j represent the x or y components of the lateral force vectors in Eqn. (11). The coefficients of the virtual pseudo-velocities in Eqn.…”

Section: Dynamic Vehicle Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lane Keeping Control Using Finite Spectrum Assignment With Modeling Errors

Vörös

Várszegi

Takács³

2019

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive

View full text Add to dashboard Cite

show abstract

“…In the field of driving action description several clear-cut situations have been studied exhaustively: lane following (Fenton, 1988;Mammar et al, 2006), car following at a safe distance (Gipps, 1981;Olstam et al, 2004), lane change (Gipps, 1986;Salvucci et al, 2007). For lane following on a curved road an extensive theory has been developed, mainly based on control engineering approaches (Hsu et al, 1998;Yuhara et al, 2001;Chen et al, 2006;Mammar et al, 2006). Yet speed control (so called longitudinal control), including speed on curves, has only been studied extensively from a car stability perspective (Jin et al, 2007;Hel et al, 2007;Song, 2008).…”

Section: Introductionmentioning

confidence: 99%

Vehicle Acceleration Prediction Using Specific Road Curvature Points

Vidugirienė

Demčenko

Tamošiūnaitė

2009

Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics

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In the work vehicle acceleration prediction issue is discussed. Three types of parameters are used for prediction system input: CAN-bus parameters-speed and curvature, derived speed parameters and newly offered specific curve point parameters, denoting changes in a curve. The real road data was used for predictions. Road curvature segments were divided into single and S-type curves. Acceleration was predicted using artificial neural networks and look-up table. The look-up table method showed the best results with newly offered specific curve parameters.

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“…If the updating rate can be boosted, information from camera can be adopted for electric vehicle motion control that needs fast sensor feedback. In addition, the performances of lane keeping and collision avoidance systems can be enhanced for traditional vehicles [4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, estimation methods for vehicle body slip angle have been extensively studied during the last few decades, and most of them are based on bicycle model [2][3]; however, such model suffers from model uncertainty problems because it includes uncertain parameters, for example, tire cornering stiffness. Other kinds of prevailing sensors such as camera, although demonstrated high accuracy on position estimation for lane keeping [4], are seldom utilized for EV motion control due to their low throughput characteristic and the image processing often takes time. On the other hand, in contradiction to vehicle bicycle model, visual model is purely based on simple geometry which contains much fewer uncertainties and the estimation results can hence be more robust against bicycle model uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Vision-based vehicle body slip angle estimation with multi-rate Kalman filter considering time delay

Wang

Nguyen

Fujimoto

et al. 2012

2012 IEEE International Symposium on Industrial Electronics

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Abstract-Body slip angle is one of the most important information for vehicle motion control; as specific sensors for body slip angle measurement are expensive, it is necessary to investigate estimation methods using existing popular sensors such as gyro sensor, encoder, camera, etc. For EV (electric vehicle), in particular, the motor response is several milliseconds which enables high performance control with short control period; fast signal feedback is consequently desired. Nevertheless, the sampling rate of a normal camera is much slower compared with other kinds of onboard sensors and the time delay caused by image processing cannot be neglected. In this paper, the two problems are solved using a multi-rate Kalman filter with measurement delay included; the estimated body slip angle can be updated every 1 ms. First of all, vehicle model and visual model are explained followed with experimental setup introduction; then, real-time image processing techniques are briefly introduced; and then, singlerate and multi-rate Kalman filters considering time delay are designed to estimate body slip angle; finally, conclusion and further works are presented.

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Analyses of Vision-based Lateral Control for Automated Highway System

Cited by 18 publications

References 6 publications

Lane Keeping Control Using Finite Spectrum Assignment With Modeling Errors

Lane Keeping Control Using Finite Spectrum Assignment With Modeling Errors

Vehicle Acceleration Prediction Using Specific Road Curvature Points

Vision-based vehicle body slip angle estimation with multi-rate Kalman filter considering time delay

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