Ship Evolutionary Trajectory Planning Method with Application of Polynomial Interpolation

2015 ◽  
pp. 161-166 ◽  
Author(s):  
P Kolendo ◽  
R Śmierzchalski
2013 ◽  
Vol 21 (1) ◽  
pp. 20-27
Author(s):  
Rafał Szłapczyński

ABSTRACT In the previous paper the author presented the evolutionary ship trajectory planning method designed to support Traffic Separation Schemes (TSS). This time the extensions of this method are described which allow to combine evolutionary trajectory planning with speed reduction manoeuvres. On TSS regions with higher than usual density of traffic and smaller distances between ships, the course alterations alone are not always sufficient or effective means of collision avoidance. Therefore they must be supplemented by speed reduction manoeuvres to a larger extent than on open waters. The paper includes a brief description of the optimisation problem, descriptions of the new elements of the method (fitness function, algorithms and the evolutionary cycle) and the examples of how the extended method successfully solves the problems unsolvable without applying speed reduction.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan Fang ◽  
Lan Yang ◽  
Tianqi Wang ◽  
Shoucai Jing

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.


2021 ◽  
Author(s):  
Heqiang Tian ◽  
Jingbo Pan ◽  
Yu Gao ◽  
Bin Tian ◽  
Debao Meng ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document