Evaluation of Path Tracking Performance of a Self-driving Tracked Vehicle

2021 ◽  
Vol 45 (12) ◽  
pp. 1167-1176
Author(s):  
Jun Ha Sohn ◽  
Chang-Ho Lee ◽  
Yong-Joo Kim ◽  
Sung-Soo Kim
Keyword(s):  
Path Tracking ◽  
Tracking Performance ◽  
Tracked Vehicle

The Study on Co-Simulation Based Tracked Vehicle Path Tracking Control

2015 ◽  
Author(s):  
Can Wang ◽  
Bo Yang ◽  
Gangfeng Tan ◽  
YiRui Wang ◽  
Li Zhou
Keyword(s):  
Tracking Control ◽  
Path Tracking ◽  
Tracked Vehicle ◽  
Simulation Based ◽  
Vehicle Path

Human-Centered Design of a Spine Biopsy Simulator and the Effects of Visual and Force Feedback on Path-Tracking Performance

2000 ◽  
Vol 9 (4) ◽  
pp. 337-349 ◽  
Author(s):  
Corinna Lathan ◽  
Kevin Cleary ◽  
Laura Traynor
Keyword(s):  
Time Delay ◽  
Visual Feedback ◽  
Task Analysis ◽  
Human Performance ◽  
Force Feedback ◽  
Spatial Reasoning ◽  
Path Tracking ◽  
Tracking Performance ◽  
Tissue Samples ◽  
Technological Intervention

Computed tomography (CT)-directed needle biopsies are routinely performed to gather tissue samples near the spine. As currently practiced, this procedure requires a great deal of spatial reasoning, skill, and training on the part of the interventional radiologist. Our goal was to evaluate the procedure through a task analysis and to make recommendations as to how the procedure could be improved through technological intervention. To this end, a spine biopsy surgical simulator was developed to mimic the current procedure and to serve as a development testbed for procedure innovation. Our methods for looking at the biopsy procedure itself included a task analysis (which produces a detailed list of tasks needed to complete a goal, their order, and time to completion) and an evaluation of human performance measures related to our simulator interface. Experiments were run to examine the effects of force and visual feedback on path-tracking performance and to determine the effects of time delay in the visual feedback on path-tracking performance. Force feedback improved performance in the conditions with visual feedback and in the conditions with visual feedback and time delay.

Enhancing path tracking performance by using haptic shared steering control combined with DYC

2016 ◽  
pp. 49-54
Author(s):  
S. Inoue ◽  
H. Inoue ◽  
T. Ozawa ◽  
P. Raksincharoensak ◽  
M. Nagai
Keyword(s):  
Path Tracking ◽  
Tracking Performance ◽  
Steering Control

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

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 144
Author(s):  
Taewon Ahn ◽  
Yongki Lee ◽  
Kihong Park
Keyword(s):  
Control System ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Safety Margin ◽  
Autonomous Driving ◽  
Path Tracking ◽  
Tracking Performance ◽  
Change Scenario ◽  
Vehicle Stability ◽  
Yaw Moment

This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and path tracking performance. The vehicle is assumed to employ an IWM independently at each wheel. The AD controller implements longitudinal/lateral path tracking using proportional-integral(PI) control and adaptive model predictive control. The chassis controller is composed of two lateral control units: the active front steering (AFS) control and the torque vectoring (TV) control. Jointly, they find the yaw moment to maintain vehicle stability using sliding mode control; AFS is prioritized over TV to enhance safety margin and energy saving. Then, the command yaw moment is optimally distributed to each wheel by solving a constrained least-squares problem. Validation was performed using simulation in a double lane change scenario. The simulation results show that the integrated AD control system of this paper significantly improves the path tracking capability and vehicle stability in comparison with other control systems.

scholarly journals A Variable-Sampling Time Model Predictive Control Algorithm for Improving Path-Tracking Performance of a Vehicle

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6845
Author(s):  
Yoonsuk Choi ◽  
Wonwoo Lee ◽  
Jeesu Kim ◽  
Jinwoo Yoo
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Path Following ◽  
Path Tracking ◽  
Sampling Time ◽  
Tracking Performance ◽  
Control Input ◽  
Automated Driving ◽  
Lateral Velocity ◽  
Time Model

This paper proposes a novel model predictive control (MPC) algorithm that increases the path tracking performance according to the control input. The proposed algorithm reduces the path tracking errors of MPC by updating the sampling time of the next step according to the control inputs (i.e., the lateral velocity and front steering angle) calculated in each step of the MPC algorithm. The scenarios of a mixture of straight and curved driving paths were constructed, and the optimal control input was calculated in each step. In the experiment, a scenario was created with the Automated Driving Toolbox of MATLAB, and the path-following performance characteristics and computation times of the existing and proposed MPC algorithms were verified and compared with simulations. The results prove that the proposed MPC algorithm has improved path-following performance compared to those of the existing MPC algorithm.

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