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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Main Authors: Heungseok Chae, Kyongsu Yi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9034121/
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spelling 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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