A self-learning dynamic path planning method for evacuation in large public buildings based on neural networks

2019 ◽  
Vol 365 ◽  
pp. 71-85 ◽  
Author(s):  
Yang Peng ◽  
Sun-Wei Li ◽  
Zhen-Zhong Hu
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 9046-9060
Author(s):  
Zhi Cai ◽  
Xuerui Cui ◽  
Xing Su ◽  
Qing Mi ◽  
Limin Guo ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi-qiang Liu ◽  
Teng Zhang ◽  
Yi-fan Wang

A local dynamic path planning method is proposed to compensate for the lack of consideration of the movement state of surrounding vehicles, the poor comfort, and the low traffic efficiency when the existing vehicle changes lanes automatically. Firstly, the cubic polynomial is predefined, and the optimal track path is solved. According to the real-time information of environment perception, the model is continuously modified by acquiring real-time information in the course of path planning, and the regional safety of the vehicle is realized. The Carsim and simulink simulation results and actual vehicle verification show that, compared with the traditional nondynamic research method, this method can effectively solve the problem that the vehicle speed variation and the sudden intrusions of the vehicle leading to the compulsory operation of the vehicle during the course of lane-changing. The safety is also improved. In order to ensure the vehicle comfort and stability, the lane-changing time is shortened by 20%, and the efficiency of lane-changing is improved obviously.


2018 ◽  
Vol 29 (9) ◽  
pp. 095105 ◽  
Author(s):  
Ma Teng ◽  
Li Ye ◽  
Jiang Yanqing ◽  
Wang Rupeng ◽  
Cong Zheng ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chuanhu Niu ◽  
Aijuan Li ◽  
Xin Huang ◽  
Wei Li ◽  
Chuanyan Xu

Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A ∗ algorithm. First, this method improves the heuristic function of the traditional A ∗ algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved A ∗ algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified A ∗ algorithm and the traditional A ∗ algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.


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