Steering Control of a Vehicle Equipped with Automated Lane Centering System

Cumali, Kadir; Armagan, Ersin

doi:10.23919/eleco47770.2019.8990555

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…The model comprises five 2D-Convolutional Layers, one flatten layer, one dropout layer and three Dense Layers. The initial 2D-Convolutional layer includes 24 filters, each of size (5,5), with a stride of (2, 2). The second layer is another 2D-Convolutional layer with 36 filters, each of size (5,5), and a stride of (2, 2).…”

Section: Network Architecturementioning

confidence: 99%

“…The initial 2D-Convolutional layer includes 24 filters, each of size (5,5), with a stride of (2, 2). The second layer is another 2D-Convolutional layer with 36 filters, each of size (5,5), and a stride of (2, 2). The third 2D-Convolutional layer consists of 48 filters, each of size (5,5).…”

Section: Network Architecturementioning

confidence: 99%

“…The second layer is another 2D-Convolutional layer with 36 filters, each of size (5,5), and a stride of (2, 2). The third 2D-Convolutional layer consists of 48 filters, each of size (5,5). The fourth 2D-Convolutional layer comprises 64 filters, each of size (3,3), with a default stride of (1,1).…”

Section: Network Architecturementioning

confidence: 99%

“…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%

See 3 more Smart Citations

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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Section: Network Architecturementioning

confidence: 99%

Section: Network Architecturementioning

confidence: 99%

Section: Network Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Multi-Objective Path Tracking Control for Car-Like Vehicles With Differentially Bounded n-Smooth Output

Festl,

Stolz,

Watzenig

2024

IEEE Trans. Intell. Transport. Syst.

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Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Using Electric Vehicles

et al. 2022

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Robust lane-following algorithms are one of the main challenges in developing effective automated vehicles. In this work, a team of four undergraduate students designed and evaluated several automated lane-following algorithms using computer vision as part of a Research Experience for Undergraduates program funded by the National Science Foundation. The developed algorithms use the Robot Operating System (ROS) and the OpenCV library in Python to detect lanes and implement the lane-following logic on the road. The algorithms were tested on a real-world test course using a street-legal vehicle with a high-definition camera as input and a drive-by-wire system for output. Driving data were recorded to compare the performance of human driving to that of the self-driving algorithms on the basis of three criteria: lap completion time, lane positioning infractions, and speed limit infractions. The evaluation of the data showed that the human drivers successfully completed every lap with zero infractions at a 100% success rate in varied weather conditions, whereas, our most reliable algorithms had a success rate of at least 70% with some lane positioning infractions and at lower speeds.

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Steering Control of a Vehicle Equipped with Automated Lane Centering System

Cited by 3 publications

References 10 publications

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

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

Multi-Objective Path Tracking Control for Car-Like Vehicles With Differentially Bounded n-Smooth Output

Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Using Electric Vehicles

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