A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles

Fényes, Dániel; Németh, Balázs; Gáspár, Péter

doi:10.3390/en14020517

Cited by 13 publications

(8 citation statements)

References 28 publications

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“…Instead of using parameters in physical-based relationships, a possible way to achieve the traffic flow model is to tune the model parameters through the learning process. It requests enhanced learning features, with which the structure and the parameters of the model can be automatically selected, as can be seen in, e.g., [33]. Using this improvement, the advantages of the model-based prediction on an increased horizon can be exploited.…”

Section: Discussionmentioning

confidence: 99%

Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

et al. 2021

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This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.

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Section: Discussionmentioning

confidence: 99%

Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

et al. 2021

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“…2021, 11, x FOR PEER REVIEW 3 of 19 information technologies applied for mapping out the feasible paths of autonomous vehicles. Updated references on vehicle trajectory generation and advanced autonomous vehicle control techniques are referred to in [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Vehicle Kinematic Modelmentioning

confidence: 99%

“…The vehicle travels from the starting (x 0 , y 0 , θ 0 , φ 0 ) at time t = 0 to the destination point (x T , y T , θ T , φ T ) at time t = T similarly to the system in (26) from the new starting point at (z 1.0 , z 2.0 , z 3.0 , z 4.0 ) to the new destination point at (z 1.T , z 2.T , z 3.T , z 4.T ).…”

Section: Speed Quartic Polynomial Trajectory Generationmentioning

confidence: 99%

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Feasible Trajectories Generation for Autonomous Driving Vehicles

et al. 2021

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This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems.

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“…Previous studies presented that energy minimization is a critical area of autonomous transport system development, where advanced longitudinal and lateral vehicle control methods will play a key role in achieving expected results [1][2][3][4][5][6][7]. Conversely, numerous research papers propose to improve the efficiency of the vehicle control process through the development of sensor systems and image detection methods [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

2021

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Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

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A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles

Cited by 13 publications

References 28 publications

Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

Feasible Trajectories Generation for Autonomous Driving Vehicles

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

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