Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrati...
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Online Access: | https://www.mdpi.com/2076-3417/11/8/3703 |
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doaj-bcf5b3df75734f6498722a49ab6ee9ea2021-04-20T23:01:46ZengMDPI AGApplied Sciences2076-34172021-04-01113703370310.3390/app11083703Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous DrivingDong-Sung Pae0Geon-Hee Kim1Tae-Koo Kang2Myo-Taeg Lim3Department of Software, Sangmyung University, Cheonan 31066, KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, KoreaDepartment of Human Intelligence and Robot Engineering, Sangmyung University, Cheonan 31066, KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, KoreaPath planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame.https://www.mdpi.com/2076-3417/11/8/3703path planningmodel predictive controlobstacle avoidancevehicle dynamicscomfort level |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dong-Sung Pae Geon-Hee Kim Tae-Koo Kang Myo-Taeg Lim |
spellingShingle |
Dong-Sung Pae Geon-Hee Kim Tae-Koo Kang Myo-Taeg Lim Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving Applied Sciences path planning model predictive control obstacle avoidance vehicle dynamics comfort level |
author_facet |
Dong-Sung Pae Geon-Hee Kim Tae-Koo Kang Myo-Taeg Lim |
author_sort |
Dong-Sung Pae |
title |
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving |
title_short |
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving |
title_full |
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving |
title_fullStr |
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving |
title_full_unstemmed |
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving |
title_sort |
path planning based on obstacle-dependent gaussian model predictive control for autonomous driving |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-04-01 |
description |
Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame. |
topic |
path planning model predictive control obstacle avoidance vehicle dynamics comfort level |
url |
https://www.mdpi.com/2076-3417/11/8/3703 |
work_keys_str_mv |
AT dongsungpae pathplanningbasedonobstacledependentgaussianmodelpredictivecontrolforautonomousdriving AT geonheekim pathplanningbasedonobstacledependentgaussianmodelpredictivecontrolforautonomousdriving AT taekookang pathplanningbasedonobstacledependentgaussianmodelpredictivecontrolforautonomousdriving AT myotaeglim pathplanningbasedonobstacledependentgaussianmodelpredictivecontrolforautonomousdriving |
_version_ |
1721517300372209664 |