Lane centering assistance system design for large speed variation and curved roads

Ballesteros-Tolosana, Iris; Rodríguez-Ayerbe, Pedro; Olaru, Sorin; Deborne, Renaud; Pita-Gil, Guillermo

doi:10.1109/ccta.2017.8062474

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Remark 3.1: By defining the lumped disturbanced, the system matrices in (3) depend on a single time-varying parameter v. This will allow us to reformulate (3) as an LPV system (4) with a single scheduling parameter. Therefore, the proposed LK design is less complex than the existing designs with two [9] or three [10] scheduling parameters.…”

Section: A Lk Controllermentioning

confidence: 99%

“…To reduce the conservativeness, the lateral dynamics can be approximated by a convex combination of the linear models associated with the minimum and maximum longitudinal velocities. In this way, LK can be realized using a linear parameter varying (LPV) controller [9] or a fuzzy static output feedback controller [10]. However, road bank is not considered in [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…However, the above observers are designed under a constant longitudinal velocity. To deal with time-varying longitudinal velocity, LPV observer [9] and fuzzy observer [14] are developed by using the approximated lateral models. However, the influence of road bank, sensor bias and noise are not considered in their designs.…”

Section: Introductionmentioning

confidence: 99%

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Robust Cooperative Adaptive Cruise Control of Vehicles on Banked and Curved Roads with Sensor Bias

Lan

Zhao

2020

2020 American Control Conference (ACC)

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This paper considers cooperative adaptive cruise control (CACC) for automated vehicles on banked and curved roads. In such context, the existing longitudinal control alone cannot guarantee robustly stable CACC, because the vehicle longitudinal dynamics are coupled with the lateral dynamics. This gives rise to the necessity of incorporating the lane keeping (LK) control with CACC for the following vehicle to guarantee lateral stability and adaptive cruise. A robustly independent design strategy is proposed for determining a linear parameter varying (LPV) LK controller and a constant gain CACC controller. The influence of sensor bias and noise is also considered and a linear time-varying generalized augmented state observer (LTV-GASO) is developed to obtain optimal state estimation of the CACC and LK systems. Designs of the observers and controllers for CACC and LK systems follow a similar scheme with an easily solved linear optimization problem formulation. This facilitates the overall vehicle control system design and implementation. Simulation of a platoon in actual traffic environment illustrates efficacy of the proposed designs in achieving robustly stable vehicle following and lateral stability.

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Section: A Lk Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Cooperative Adaptive Cruise Control of Vehicles on Banked and Curved Roads with Sensor Bias

Lan

Zhao

2020

2020 American Control Conference (ACC)

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“…The evolution of autonomous driving technology has been marked by the continuous integration of advanced features aimed at enhancing vehicle safety and performance. Some example features are adaptive cruise control (ACC), [1][2][3] lane centering control (LCC), [4][5][6] automatic intersection navigation (AIN), 7,8 etc. Lane centering is a critical component in autonomous navigation systems that assists vehicles in maintaining their position within designated lanes.…”

Section: Introductionmentioning

confidence: 99%

CNN-based end-to-end learning for lane centering

Ebu,

Islam,

Ball

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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Lane centering is a significant feature in the automotive industry and an important feature for advanced or autonomous vehicles, providing assistance to help drivers stay in their lane. The objective of this work is to utilize camera images which are processed by a lane-centering algorithm to make steering decisions. A model with a convolutional neural network (CNN) algorithm is used and simulated datasets are trained and tested. The goal is to make the program learn how to steer the vehicle autonomously and achieve end-to-end learning for steering command generation. The CNN can map raw pixels from cameras directly to steering commands without any intermediate feature engineering. A model is utilized which is created by NVIDIA researchers called the NVIDIA PilotNet. This network comprises five convolutional layers for feature extraction, followed by three fully connected layers for predicting the steering commands. The model is trained using two different sets of data to see how well the model performs with different types of data. The first set comes from Udacity's Self-Driving Car Nanodegree Program, which uses their open-source Vehicle Simulation.Secondly, a dataset from the Mississippi State University Autonomous Vehicular Simulator (MAVS) is used. The training process involves reducing the error between predicted steering angles and the actual steering commands logged by the car. After the model has been trained, it is implemented for testing the car's autonomous capabilities within the Self Driving Car Nanodegree Program Simulation. In this mode, the car demonstrates the ability to effectively track and navigate along the road lanes.

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Nonlinear Vehicle Trajectory Guidance for Automated Driving on Highways

Schucker¹,

Konigorski

2018

IFAC-PapersOnLine

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Lane centering assistance system design for large speed variation and curved roads

Cited by 5 publications

References 24 publications

Robust Cooperative Adaptive Cruise Control of Vehicles on Banked and Curved Roads with Sensor Bias

Robust Cooperative Adaptive Cruise Control of Vehicles on Banked and Curved Roads with Sensor Bias

CNN-based end-to-end learning for lane centering

Nonlinear Vehicle Trajectory Guidance for Automated Driving on Highways

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