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Path Planning and Tracking Control for Automatic Driving Obstacle Avoidance

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RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

Pages 300 - 304

https://doi.org/10.1145/3366194.3366247

Published: 20 September 2019 Publication History

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Abstract

This paper studies the path planning and tracking control of obstacle avoidance lane change in intelligent auto driving. Firstly, the vehicle two-degree-of-freedom model of front wheel steering is established by the knowledge of automobile kinematics and dynamics. Secondly, the trajectory planning is realized by the fifth-order polynomial curve fitting method. Then the linear time-varying model predictive control method is used to realize the tracking control of the trajectory. Finally, MATLAB simulation is used to verify the feasibility of the above ideas. The experimental results show that the fifth-order polynomial can quickly and accurately fit the ideal curve; the model predictive control can also stably control the car to follow the prescribed path.

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Cited By

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Zhang HYao JTian S(2024)Model Predictive Control for Trajectory Planning Considering Constraints on Vertical Load VariationElectronics10.3390/electronics1308148813:8(1488)Online publication date: 14-Apr-2024
https://doi.org/10.3390/electronics13081488
Biniewicz JPyrz M(2024)Influence of the Road Model on the Optimal Maneuver of a Racing MotorcycleApplied Sciences10.3390/app1410400614:10(4006)Online publication date: 8-May-2024
https://doi.org/10.3390/app14104006
Tang LWang ZDu H(2022)Design of path planning and tracking control for a UGVProceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence10.1145/3584376.3584410(193-199)Online publication date: 16-Dec-2022
https://dl.acm.org/doi/10.1145/3584376.3584410

Index Terms

Path Planning and Tracking Control for Automatic Driving Obstacle Avoidance
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Computational control theory
      2. Motion path planning

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Published In

RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 2019

803 pages

ISBN:9781450372985

DOI:10.1145/3366194

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2019

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RICAI 2019

RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 20 - 22, 2019

Shanghai, China

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RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;

Overall Acceptance Rate 140 of 294 submissions, 48%

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Cited By

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Zhang HYao JTian S(2024)Model Predictive Control for Trajectory Planning Considering Constraints on Vertical Load VariationElectronics10.3390/electronics1308148813:8(1488)Online publication date: 14-Apr-2024
https://doi.org/10.3390/electronics13081488
Biniewicz JPyrz M(2024)Influence of the Road Model on the Optimal Maneuver of a Racing MotorcycleApplied Sciences10.3390/app1410400614:10(4006)Online publication date: 8-May-2024
https://doi.org/10.3390/app14104006
Tang LWang ZDu H(2022)Design of path planning and tracking control for a UGVProceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence10.1145/3584376.3584410(193-199)Online publication date: 16-Dec-2022
https://dl.acm.org/doi/10.1145/3584376.3584410

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Adaptive path planning and tracking system based on model predictive control for autonomous vehicle local obstacle avoidance

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Path planning with obstacle avoidance by G 1 PH quintic splines