Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles
This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These...
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doaj-26e11910c4e14ea4b2e0a8b3a4afeaa02021-03-30T02:14:01ZengIEEEIEEE Access2169-35362020-01-018513635137610.1109/ACCESS.2020.29803919034121Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous VehiclesHeungseok Chae0Kyongsu Yi1https://orcid.org/0000-0001-5931-6809Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaDepartment of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaThis paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These are considered through safe distance based on human driving behavior. Since all vehicles cannot be equipped with a vehicle to vehicle communications at present, autonomous vehicles should perceive the surrounding environment based on local sensors. In this paper, virtual targets are devised to cope with the limitation of cognitive range. A probabilistic prediction is adopted to enhance safety, given the potential behavior of surrounding vehicles. Then, decision-making and motion planning has been designed based on the probabilistic prediction-based safe distance, which could achieve safety performance without a heavy computational burden. The algorithm has considered the decision rules that drivers use when overtaking. For this purpose, concepts of target space, demand, and possibility for lane change are devised. In this paper, three driving modes are developed for active overtaking. The desired driving mode is decided for safe and efficient overtaking. To obtain desired states and constraints, intuitive motion planning is conducted. A stochastic model predictive control has been adopted to determine vehicle control inputs. The proposed autonomous overtaking algorithm has been evaluated through simulation, which reveals the effectiveness of virtual targets. Also, the proposed algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable overtaking driving has been demonstrated using a test vehicle.https://ieeexplore.ieee.org/document/9034121/Autonomous vehiclesautonomous drivingdecision-makingmotion planningvehicle controlovertaking |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Heungseok Chae Kyongsu Yi |
spellingShingle |
Heungseok Chae Kyongsu Yi Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles IEEE Access Autonomous vehicles autonomous driving decision-making motion planning vehicle control overtaking |
author_facet |
Heungseok Chae Kyongsu Yi |
author_sort |
Heungseok Chae |
title |
Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles |
title_short |
Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles |
title_full |
Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles |
title_fullStr |
Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles |
title_full_unstemmed |
Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles |
title_sort |
virtual target-based overtaking decision, motion planning, and control of autonomous vehicles |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These are considered through safe distance based on human driving behavior. Since all vehicles cannot be equipped with a vehicle to vehicle communications at present, autonomous vehicles should perceive the surrounding environment based on local sensors. In this paper, virtual targets are devised to cope with the limitation of cognitive range. A probabilistic prediction is adopted to enhance safety, given the potential behavior of surrounding vehicles. Then, decision-making and motion planning has been designed based on the probabilistic prediction-based safe distance, which could achieve safety performance without a heavy computational burden. The algorithm has considered the decision rules that drivers use when overtaking. For this purpose, concepts of target space, demand, and possibility for lane change are devised. In this paper, three driving modes are developed for active overtaking. The desired driving mode is decided for safe and efficient overtaking. To obtain desired states and constraints, intuitive motion planning is conducted. A stochastic model predictive control has been adopted to determine vehicle control inputs. The proposed autonomous overtaking algorithm has been evaluated through simulation, which reveals the effectiveness of virtual targets. Also, the proposed algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable overtaking driving has been demonstrated using a test vehicle. |
topic |
Autonomous vehicles autonomous driving decision-making motion planning vehicle control overtaking |
url |
https://ieeexplore.ieee.org/document/9034121/ |
work_keys_str_mv |
AT heungseokchae virtualtargetbasedovertakingdecisionmotionplanningandcontrolofautonomousvehicles AT kyongsuyi virtualtargetbasedovertakingdecisionmotionplanningandcontrolofautonomousvehicles |
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1724185498957643776 |