Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation Characteristics
Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one o...
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2020-08-01
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doaj-afaef10b57034bbd86f474155eb376732020-11-25T03:55:03ZengMDPI AGSensors1424-82202020-08-01204821482110.3390/s20174821Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation CharacteristicsQinyu Sun0Yingshi Guo1Rui Fu2Chang Wang3Wei Yuan4School of Automobile, Chang’an University, Xi’an 710064, ChinaSchool of Automobile, Chang’an University, Xi’an 710064, ChinaSchool of Automobile, Chang’an University, Xi’an 710064, ChinaSchool of Automobile, Chang’an University, Xi’an 710064, ChinaSchool of Automobile, Chang’an University, Xi’an 710064, ChinaDeveloping a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.https://www.mdpi.com/1424-8220/20/17/4821Autonomous vehicleobstacle avoidanceartificial potential fieldmodel predictive controlhuman-like |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qinyu Sun Yingshi Guo Rui Fu Chang Wang Wei Yuan |
spellingShingle |
Qinyu Sun Yingshi Guo Rui Fu Chang Wang Wei Yuan Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation Characteristics Sensors Autonomous vehicle obstacle avoidance artificial potential field model predictive control human-like |
author_facet |
Qinyu Sun Yingshi Guo Rui Fu Chang Wang Wei Yuan |
author_sort |
Qinyu Sun |
title |
Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation Characteristics |
title_short |
Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation Characteristics |
title_full |
Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation Characteristics |
title_fullStr |
Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation Characteristics |
title_full_unstemmed |
Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the River’s Operation Characteristics |
title_sort |
human-like obstacle avoidance trajectory planning and tracking model for autonomous vehicles that considers the river’s operation characteristics |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-08-01 |
description |
Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs. |
topic |
Autonomous vehicle obstacle avoidance artificial potential field model predictive control human-like |
url |
https://www.mdpi.com/1424-8220/20/17/4821 |
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