Lateral Collision Avoidance Robust Control of Electric Vehicles Combining a Lane-Changing Model Based on Vehicle Edge Turning Trajectory and a Vehicle Semi-Uncertainty Dynamic Model

2018 ◽  
Vol 19 (2) ◽  
pp. 331-343 ◽  
Author(s):  
Yufeng Lian ◽  
Xiaoyu Wang ◽  
Yantao Tian ◽  
Keping Liu
Keyword(s):  
Robust Control ◽  
Dynamic Model ◽  
Collision Avoidance ◽  
Electric Vehicles ◽  
Lane Changing ◽  
Model Based

Research on active collision avoidance algorithm for intelligent vehicle based on improved artificial potential field model

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091123
Author(s):  
Chaochun Yuan ◽  
Shuofeng Weng ◽  
Jie Shen ◽  
Long Chen ◽  
Youguo He ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Superior Performance ◽  
Intelligent Vehicle ◽  
Artificial Potential Field ◽  
Sideslip Angle ◽  
Lane Changing ◽  
Model Based ◽  
Braking Process

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

Collision Cone Based Lane Changing Model for Collision Avoidance

2017 ◽  
Author(s):  
Gihyeob An ◽  
Reza Langari
Keyword(s):  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Free Space ◽  
Line Of Sight ◽  
Lane Changing ◽  
Model Based ◽  
On Line ◽  
Cone Algorithm

In this paper, we propose a lane changing model based on the collision cone approach. Specifically, we show how a vehicle decides whether to change lanes using the collision cone algorithm based on the information of the velocity and the location of surrounding vehicles. The model compares the current and target lane with a new measure of driving advantages. It determines if there are any existing driving advantages such as free space and speed by lane changing. Moreover, a new methodology of lane changing for collision avoidance, which is based on line of sight (LOS) with a new leader in a target lane, is suggested with a model predictive control (MPC) controller. Additionally, we show that the model makes reliable decisions and generates acceptable lane changing trajectories.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

A fuzzy dynamic model based state estimator

2001 ◽  
Vol 122 (1) ◽  
pp. 45-72 ◽  
Author(s):  
Jeffery R. Layne ◽  
Kevin M. Passino
Keyword(s):  
Dynamic Model ◽  
State Estimator ◽  
Model Based
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