FUZZY/CLASSIC HYBRID CONTROLLER FOR THE LONGITUDINAL CONTROL OF AN AUTONOMOUS VEHICLE

There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

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Derivation and Evaluation of a Poincare-Bendixson Theorem Based Target Acceleration Computation Algorithm for Autonomous Driving on Inverse TTC and Time Headway Plane

ASME 2020 29th Conference on Information Storage and Processing Systems ◽

10.1115/isps2020-1927 ◽

2020 ◽

Author(s):

Hye-Won Lee ◽

Kwang-Seok Oh ◽

Young-Min Yoon ◽

Kyong-Su Yi

Keyword(s):

Real Time ◽

Control Algorithm ◽

Human Factor ◽

Autonomous Vehicle ◽

Autonomous Driving ◽

Longitudinal Control ◽

Time To Collision ◽

Time Headway ◽

Target Acceleration ◽

Computation Algorithm

Abstract This paper describes derivation algorithm and evaluation results of a Poincare-Bendixson theorem based target acceleration computation algorithm for autonomous driving on inverse time to collision and time headway plane. Derivation of target acceleration is needed for longitudinal autonomous driving. Ellipsoidal driving area is derived for considering driver’s driving characteristic and safety in time headway-inverse time to collision (TTC) plane. And target acceleration computation algorithm has been proposed based on Poincare-Bendixson theorem. Ellipsoidal driving areas are divided main driving area and real-time driving area. Main driving area is derived based on limit of inverse TTC and time headway for takeover time and human factor, real-time driving area is derived through current driving point with ratio of main driving area. It is designed to computation the target acceleration after deriving the target direction by applying a specific angle based on the normal to the current driving point through the real-time driving area. Specific angle is arbitrary value applied acceleration limitation of actual vehicle. The performance evaluation of target acceleration computation algorithm is has been conducted in Matlab/Simulink environment. It is expected that the proposed algorithm can be used for longitudinal control algorithm for safety and personalization of autonomous vehicle.

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A historical review on lateral and longitudinal control of autonomous vehicle motions

2010 International Conference on Mechanical and Electrical Technology ◽

10.1109/icmet.2010.5598396 ◽

2010 ◽

Cited By ~ 42

Author(s):

Alireza Khodayari ◽

Ali Ghaffari ◽

Sina Ameli ◽

Jamal Flahatgar

Keyword(s):

Autonomous Vehicle ◽

Historical Review ◽

Longitudinal Control

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A Systematic Multi-Vehicle Platooning and Platoon Merging: Strategy, Control, and Trajectory Generation

Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing ◽

10.1115/dscc2014-6336 ◽

2014 ◽

Cited By ~ 2

Author(s):

Mohammad Goli ◽

Azim Eskandarian

Keyword(s):

Control Algorithm ◽

Autonomous Vehicle ◽

Trajectory Generation ◽

Simulation Platform ◽

Longitudinal Control ◽

Vehicle Platooning ◽

Vehicle Platoon ◽

Strategy Control ◽

Automated Highway ◽

Merging Strategy

Problem of autonomous vehicle platooning in an automated highway setting has drawn many attentions, both in academia and industry, during last two decades. This paper studies the problem of vehicle platooning with a particular focus on merging control algorithm when one or several vehicle(s) merge(s) from the adjacent lane into the main vehicle platoon under longitudinal control. Different longitudinal controllers have been compared. A practical novel multi-vehicle merge-in strategy and an adaptive lateral trajectory generation method have been proposed. The proposed approach is then tested and verified in our newly developed simulation platform SimPlatoon.

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Longitudinal control of an autonomous vehicle through a hybrid fuzzy/classical controller

Proceedings of WESCON '94 ◽

10.1109/wescon.1994.403617 ◽

2002 ◽

Cited By ~ 3

Author(s):

Z. Zalila ◽

P. Lezy

Keyword(s):

Autonomous Vehicle ◽

Longitudinal Control

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System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

Sensors ◽

10.3390/s20215999 ◽

2020 ◽

Vol 20 (21) ◽

pp. 5999

Author(s):

Shoaib Azam ◽

Farzeen Munir ◽

Ahmad Muqeem Sheri ◽

Joonmo Kim ◽

Moongu Jeon

Keyword(s):

Motion Planning ◽

Obstacle Avoidance ◽

Autonomous Vehicles ◽

Experimental Validation ◽

Autonomous Vehicle ◽

Cost Effective ◽

Longitudinal Control ◽

Sensor Layer ◽

Traffic Efficiency ◽

Control Layer

In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.

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