Compensation of Sensor and Actuator Imperfections for Lane-Keeping Control Using a Kalman Filter Predictor

Solmaz, Selim; Nestlinger, Georg; Stettinger, Georg

doi:10.4271/12-04-01-0008

Cited by 3 publications

(4 citation statements)

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“…The theoretical background of the lateral control component used in the context of the presented vehicle-following system was published in [22,25]. Since then, it was successfully applied to several practical implementations such as lane keeping [26]. Therefore, only the essence is recapitulated here.…”

Section: Lateral Controlmentioning

confidence: 99%

“…Based on the linear single-track model [27] and a linearized and time-discretized path-tracking error model [25,26], a state-feedback control law δ set = − vy ψ e lat e ψ χ k lat (8) is obtained via LQR design. The path-tracking error model, according to the classification introduced in [18], is as follows: The reference is located in the vehicle's center of gravity, the look-ahead distance is along the vehicle heading, while the lateral error e lat and heading error e ψ , with respect to the reference path, are perpendicular to the vehicle heading.…”

Section: Lateral Controlmentioning

confidence: 99%

“…To account for a varying vehicle speed and a speed-dependant look-ahead distance, the feedback law ( 8) was gain scheduled and implemented using a look-up table. The dynamics of the demonstrator vehicle's steering actuator were considered by a first-order transfer function [26], introducing one additional state χ.…”

Section: Lateral Controlmentioning

confidence: 99%

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Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

2022

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In this paper, the problem of vehicle guidance by means of an external leader is described. The objective is to navigate a four-wheeled vehicle through unstructured environments, characterized by the lack of availability of typical guidance infrastructure like lane markings or HD maps. The trajectory-following approach is based on an estimate of the leader’s path. For that, position measurements are stored over time with respect to an inertial frame. A new strategy is proposed to rate the significance of position measurements and ensure that a certain threshold of stored samples is not exceeded. Having an estimate of the leader path is essential to prevent the cutting-corner phenomenon and for exact path following in general. A spline-approximation technique is applied to obtain a smooth reference path for the underlying lateral and longitudinal motion controllers. For longitudinal tracking, a constant time-headway policy was implemented, to follow the leader with a constant time gap along the estimated path. The algorithm was first developed and tested in a simulation framework and then deployed in a demonstrator vehicle for validation under real operating conditions. The presented experimental results were achieved using only on-board sensors of the demonstrator vehicle, while high-accuracy differential GPS-based position measurements serve as the ground truth data for visualization.

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Section: Lateral Controlmentioning

confidence: 99%

Section: Lateral Controlmentioning

confidence: 99%

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Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

2022

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“…For tracking the lateral and longitudinal references from the trajectory planner, two proven controller implementations were reused. The lateral controller implements a state-feedback control law based on [23], modified according to [24] as motivated from a demonstrator vehicle application. It makes use of the reference path from the trajectory planner up to the second geometric continuity, i.e., lateral error, heading error, and path curvature.…”

Section: Actuation: Lateral and Longitudinal Controlmentioning

confidence: 99%

Development and Verification of Infrastructure-Assisted Automated Driving Functions

et al. 2021

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Automated vehicles we have on public roads today are capable of up to SAE Level-3 conditional autonomy according to the SAE J3016 Standard taxonomy, where the driver is the main responsible for the driving safety. All the decision-making processes of the system depend on computations performed on the ego vehicle and utilizing only on-board sensor information, mimicking the perception of a human driver. It can be conjectured that for higher levels of autonomy, on-board sensor information will not be sufficient alone. Infrastructure assistance will, therefore, be necessary to ensure the partial or full responsibility of the driving safety. With higher penetration rates of automated vehicles however, new problems will arise. It is expected that automated driving and particularly automated vehicle platoons will lead to more road damage in the form of rutting. Inspired by this, the EU project ESRIUM investigates infrastructure assisted routing recommendations utilizing C-ITS communications. In this respect, specially designed ADAS functions are being developed with capabilities to adapt their behavior according to specific routing recommendations. Automated vehicles equipped with such ADAS functions will be able to reduce road damage. The current paper presents the specific use cases, as well as the developed C-ITS assisted ADAS functions together with their verification results utilizing a simulation framework.

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Real-life Implementation and Comparison of Authenticated Path Following for Automated Vehicles based on Galileo OSNMA Localization

Solmaz,

Nestlinger,

Diengsleder-Lambauer

et al. 2023

2023 IEEE International Automated Vehicle Validation Conference (IAVVC)

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Compensation of Sensor and Actuator Imperfections for Lane-Keeping Control Using a Kalman Filter Predictor

Cited by 3 publications

References 0 publications

Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

Development and Verification of Infrastructure-Assisted Automated Driving Functions

Real-life Implementation and Comparison of Authenticated Path Following for Automated Vehicles based on Galileo OSNMA Localization

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