scholarly journals Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4821
Author(s):  
Qinyu Sun ◽  
Yingshi Guo ◽  
Rui Fu ◽  
Chang Wang ◽  
Wei Yuan
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Trajectory Tracking ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Planning Model ◽  
Repulsive Potential ◽  
Tracking Model ◽  
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 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.

Driver Speed Control Modeling for Predictive Braking Assistance System Based on Risk Potential in Intersections

2014 ◽  
Vol 26 (5) ◽  
pp. 628-637 ◽  
Author(s):  
Pongsathorn Raksincharoensak ◽  
◽  
Yuta Akamatsu ◽  
Katsumi Moro ◽  
Masao Nagai ◽  
...  
Keyword(s):  
Potential Field ◽  
Driving Simulator ◽  
Near Miss ◽  
Assistance System ◽  
Repulsive Potential ◽  
Pedestrian Movement ◽  
Basic Parameters ◽  
Scenario Model ◽  
Control Modeling ◽  
Risk Potential

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260005/12.jpg"" width=""300"" />Predictive braking assistance system</div> This paper describes the assessment of a predictive braking assistance system, which is done using a driving simulator that reconstructs near-miss incident scenarios relevant to pedestrians. An autonomous braking assistance algorithm for collision avoidance is designed based on pedestrian movement prediction and an artificial risk potential field. A virtual spring connecting the vehicle and the pedestrian is used to determine the repulsive potential field and the intensity of the deceleration. The feasibility of the proposed braking assistance algorithm is examined through experiments using the driving simulator and a comparison to actual driving data. Near-miss incident data relevant to pedestrians in intersections are analyzed to get the basic parameters of a crash scenario model relevant to pedestrians. Driving simulator experiments are used to verify the effectiveness of the proposed system. </span>

scholarly journals An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141879956 ◽  
Author(s):  
Wenrui Wang ◽  
Mingchao Zhu ◽  
Xiaoming Wang ◽  
Shuai He ◽  
Junpei He ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Potential Field ◽  
Lyapunov Stability Theory ◽  
Field Method ◽  
Artificial Potential Field ◽  
Redundant Manipulators ◽  
Field Function ◽  
Potential Field Method ◽  
Artificial Potential Field Method

In this article, we present an improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators. Specifically, we not only focused on the position for the manipulator end-effectors but also considered their posture in the course of trajectory planning and obstacle avoidance. We introduced boundaries for Cartesian space components to optimize the attractive field function. Moreover, the manipulator achieved a reasonable speed to move to the target pose, regardless of the difference between the initial pose and target pose. We proved the stability using Lyapunov stability theory by introducing velocity feedforward, when the manipulator moved along a continuous trajectory. Considering the shape of the manipulator joints and obstacles, we set up the collision detection model by projecting the obstacles to link coordinates. In this case, establishing the repulsive field between the nearest points on every joint and obstacles with the closest distance was sufficient for achieving obstacle avoidance for redundant manipulators. The simulation results based on a nine-degree-of-freedom hyper-redundant manipulator, which was designed and made in our laboratory, fully substantiated the efficacy and superiority of the proposed method.

scholarly journals A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jang-Ho Cho ◽  
Dong-Sung Pae ◽  
Myo-Taeg Lim ◽  
Tae-Koo Kang
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Calculation Method ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Field Calculation ◽  
Gaussian Potential

A new obstacle avoidance method for autonomous vehicles calledobstacle-dependent Gaussian potential field(ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases.

scholarly journals MPC Based Trajectory Tracking for An Automonous Deep-Sea Tracked Mining Vehicle

2021 ◽  
Vol 1 (2) ◽  
pp. 1-13
Author(s):  
Hongyun Wu ◽  
Yuheng Chen ◽  
Hongmao Qin
Keyword(s):  
Obstacle Avoidance ◽  
Kalman Filtering ◽  
Trajectory Tracking ◽  
Deep Sea ◽  
Autonomous Vehicles ◽  
External Disturbance ◽  
Kinematic Model ◽  
Vehicle Body ◽  
Model Uncertainties ◽  
External Disturbances

Model predictive control (MPC) has been successfully used in trajectory tracking for autonomous vehicles based on certain kinematic model under low external disturbance conditions, but when there are model uncertainties and external disturbances, autonomous vehicles will fail to follow the pre-set trajectory. This paper studies trajectory tracking control based on MPC for an autonomous deep-sea tracked mining vehicle in polymetallic nodule mines with model uncertainty and external disturbances. A MPC algorithm is designed for trajectory tracking. To address model uncertainties caused by vehicle body subsidence and track slippage, a drive wheel speed correction controller is designed by experimental data fitting, and Kalman filtering (KF) and adaptive Kalman filtering (AKF) are introduced to improve tracking performance by rejecting external disturbances especially during curve tracking. To handle dead zones and obstacles during actual operation, an obstacle avoidance strategy is proposed that uses the tri-circular arc obstacle avoidance trajectory with an equal curvature for path re-planning. Finally, Simulink&Recurdyn co-simulations validate the performance of the proposed MPC controller through a comparison with nonlinear MPC(NMPC).

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