Global and local obstacle avoidance technique for an autonomous vehicle

1999 ◽  
Author(s):  
Keith W. Gray ◽  
Kevin S. Saunders
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Global And Local

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance

2018 ◽  
Author(s):  
Ryan P. Shaw ◽  
David M. Bevly
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Reference Trajectory ◽  
Ground Vehicles ◽  
Proportional Navigation ◽  
Predictive Controller ◽  
Proportional Navigation Law ◽  
Vehicle Dynamic Model ◽  
And Control

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.

Obstacle Avoidance in Simulated Environment Using Eye Tracking Technologies

2009 ◽  
Author(s):  
Csaba Antonya ◽  
Florin Barbuceanu ◽  
Zolta´n Rusa´k ◽  
Doru Talaba ◽  
Silviu Butnariu ◽  
...  
Keyword(s):  
Eye Movements ◽  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Computer Game ◽  
Gaze Fixation ◽  
The Gaze ◽  
Simulated Environment ◽  
Mapping System ◽  
Avoidance Strategies ◽  
Human Eye Movements

The paper is investigating the relationship between human eye movements, correlated with the visual perception of computer generated scene on one hand and obstacle avoidance strategies on the other hand, during the process of driving a computer game-like car. Several issues were investigated regarding how the gaze fixation point of the driver is moving during obstacle avoidance maneuvers. The relevance of each issue in making a decision was assessed. The main goal is to establish a correlation (mapping) system between gaze fixation parameters and obstacles avoidance strategies in order to be able to develop cognitive algorithms for driver assistance in real world driving conditions, to monitor driver’s vigilance and ultimately to enable progress towards the autonomous vehicle which can avoid possible obstacles or resolve hazardous traffic situations just by monitoring the eye movements of the driver.

Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092110
Author(s):  
Runqiao Liu ◽  
Minxiang Wei ◽  
Nan Sang
Keyword(s):  
Obstacle Avoidance ◽  
Disturbance Rejection ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Autonomous Vehicle ◽  
Angular Frequency ◽  
Vehicle Speed ◽  
Feed Forward ◽  
Yaw Rate ◽  
Active Disturbance Rejection

To solve the problem of understeer and oversteer for autonomous vehicle under high-speed emergency obstacle avoidance conditions, considering the effect of steering angular frequency and vehicle speed on yaw rate for four-wheel steering vehicles in the frequency domain, a feed-forward controller for four-wheel steering autonomous vehicles that tracks the desired yaw rate is proposed. Furthermore, the steering sensitivity coefficient of the vehicle is compensated linearly with the change in the steering angular frequency and vehicle speed. In addition, to minimize the tracking errors caused by vehicle nonlinearity and external disturbances, an active disturbance rejection control feedback controller that tracks the desired lateral displacement and desired yaw angle is designed. Finally, CarSim® obstacle avoidance simulation results show that an autonomous vehicle with the four-wheel steering path tracking controller consisting of feed-forward control and feedback control could not only improve the tire lateral forces but also reduce tail flicking (oversteer) and pushing ahead (understeer) under high-speed emergency obstacle avoidance conditions.

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