Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

Ahn, Tae-Won; Lee, Yong-ki; Park, Kihong

doi:10.3390/electronics10020144

Cited by 26 publications

(17 citation statements)

References 30 publications

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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%

“…In this ca vehicle moves along this trajectory with the maximum steering angle of 42.8547°. Th destination of the vehicle is 𝑥 𝑇𝑁 = 𝑦 𝑇𝑁 = 23.559 and this feasible trajectory is foun four interactions with 𝑛 = 4 trials in (24) and (25). Once we have calculated and selected an optimal feasible trajectory subject to constraints, we can again determine the steering angle by the vehicle velocity moving along this trajectory.…”

Section: Trajectory Generation Subject To Constraintsmentioning

confidence: 99%

“…In this case, the vehicle moves along this trajectory with the maximum steering angle of 42.8547 • . The new destination of the vehicle is x TN = y TN = 23.559 and this feasible trajectory is found after four interactions with n = 4 trials in (24) and (25).…”

Section: Trajectory Generation Subject To Constraintsmentioning

confidence: 99%

“…From now on, the vehicle steering angle is considered as the front wheel angle and has a physical limitation of +/−45 degrees. 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: Introductionmentioning

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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Section: Vehicle Kinematic Modelmentioning

confidence: 99%

Section: Trajectory Generation Subject To Constraintsmentioning

confidence: 99%

Section: Trajectory Generation Subject To Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feasible Trajectories Generation for Autonomous Driving Vehicles

et al. 2021

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“…The model predictive controller (MPC) has shown remarkable success for the control and planning of numerous robotic systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. However, its design necessitates an inevitable tuning procedure that involves the determination of its cost function weights.…”

Section: Introductionmentioning

confidence: 99%

Can Deep Models Help a Robot to Tune Its Controller? A Step Closer to Self-Tuning Model Predictive Controllers

2021

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Motivated by the difficulty roboticists experience while tuning model predictive controllers (MPCs), we present an automated weight set tuning framework in this work. The enticing feature of the proposed methodology is the active exploration approach that adopts the exploration–exploitation concept at its core. Essentially, it extends the trial-and-error method by benefiting from the retrospective knowledge gained in previous trials, thereby resulting in a faster tuning procedure. Moreover, the tuning framework adopts a deep neural network (DNN)-based robot model to conduct the trials during the simulation tuning phase. Thanks to its high fidelity dynamics representation, a seamless sim-to-real transition is demonstrated. We compare the proposed approach with the customary manual tuning procedure through a user study wherein the users inadvertently apply various tuning methodologies based on their progressive experience with the robot. The results manifest that the proposed methodology provides a safe and time-saving framework over the manual tuning of MPC by resulting in flight-worthy weights in less than half the time. Moreover, this is the first work that presents a complete tuning framework extending from robot modeling to directly obtaining the flight-worthy weight sets to the best of the authors’ knowledge.

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