A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles

2020 ◽  
Vol 141 ◽  
pp. 112953 ◽  
Author(s):  
Tao Peng ◽  
Lili Su ◽  
Ronghui Zhang ◽  
Zhiwei Guan ◽  
Honglin Zhao ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Control Method ◽  
Lane Change ◽  
Trajectory Model ◽  
Automatic Driving ◽  
Collision Avoidance Control ◽  
Avoidance Control

scholarly journals An autonomous dynamic collision avoidance control method for unmanned surface vehicle in unknown ocean environment

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983158 ◽  
Author(s):  
Yunsheng Fan ◽  
Xiaojie Sun ◽  
Guofeng Wang
Keyword(s):  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Control Method ◽  
Unmanned Surface Vehicle ◽  
Time Dynamic ◽  
Collision Avoidance Control ◽  
Dynamic Obstacle Avoidance ◽  
Avoidance Control ◽  
Dynamic Obstacle ◽  
Ocean Environment

There are many unknown obstacles in the sea, so the autonomous navigation of unmanned surface vehicle needs to avoid them as soon as possible even under the condition of low control ability of the controller. To solve the problem, by combining the dynamic collision avoidance algorithm and tracking control, a dynamic collision avoidance control method in the unknown ocean environment is presented. In this article, in consideration of the unknown ocean environment and real-time dynamic obstacle avoidance problem, the collision avoidance controller using a velocity resolution method and backstepping tracking controller based on unmanned surface vehicle maneuvering motion model is designed. Simulation results show that the method is effective and accurate and can provide the reference for the unmanned surface vehicle intelligent collision avoidance control technology.

Self-learning control for coordinated collision avoidance of automated vehicles

2019 ◽  
pp. 095440701988788
Author(s):  
Yan Wang ◽  
Guodong Yin ◽  
Yanjun Li ◽  
Saif Ullah ◽  
Weichao Zhuang ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Control Method ◽  
Learning Control ◽  
Traffic Information ◽  
Steering Control ◽  
Control Effect ◽  
Driving Experience ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Self Learning

For the improvement of automotive active safety and the reduction of traffic collisions, significant efforts have been made on developing a vehicle coordinated collision avoidance system. However, the majority of the current solutions can only work in simple driving conditions, and cannot be dynamically optimized as the driving experience grows. In this study, a novel self-learning control framework for coordinated collision avoidance is proposed to address these gaps. First, a dynamic decision model is designed to provide initial braking and steering control inputs based on real-time traffic information. Then, a multilayer artificial neural networks controller is developed to optimize the braking and steering control inputs. Next, a proportional–integral–derivative feedback controller is used to track the optimized control inputs. The effectiveness of the proposed self-learning control method is evaluated using hardware-in-the-loop tests in different scenarios. Experimental results indicate that the proposed method can provide good collision avoidance control effect. Furthermore, vehicle stability during the coordinated collision avoidance control can be gradually improved by the self-learning method as the driving experience grows.

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

2021 ◽  
pp. 095440702110240
Author(s):  
Ziyu Zhang ◽  
Chunyan Wang ◽  
Wanzhong Zhao ◽  
Jian Feng
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Decentralized Control ◽  
Control Strategy ◽  
Centralized Control ◽  
Lateral Control ◽  
Time Performance ◽  
Collision Avoidance Control ◽  
Avoidance Control ◽  
Control Accuracy

In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

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