scholarly journals Target Lane Changing Prediction Method for ACC System

Author(s):  
Li Li ◽  
Xiao Ren
2018 ◽  
Vol 10 (9) ◽  
pp. 168781401880293 ◽  
Author(s):  
Wei Yuan ◽  
Zhen Li ◽  
Chang Wang

A test platform with a millimeter-wave radar sensor, lane-line sensor, gyroscope, and controller area network was established to improve safety in using adaptive cruise control systems in vehicles. The motion-state characterization data of the host vehicle and surrounding vehicles in a real traffic environment were captured. The prediction method for the lane-changing maneuver of the vehicle ahead was developed using a hidden Markov model based on the distance between the host vehicle and the front vehicle, as well as the lateral and longitudinal velocities of the vehicle in front. The adaptive cruise control system control algorithm for assessing the target vehicle was optimized. The model was tested, and its predictions were compared with measured data. Result shows that the lane-changing and lane-keeping behaviors of the vehicle ahead can be predicted efficiently and accurately by the model. The maximum prediction accuracy rate for straight roads was 97% with the time window length of 4.5 s, whereas that for curved roads was 96% with the time window length of 3.5 s.


2021 ◽  
Author(s):  
junzhe lu ◽  
jiangtian li ◽  
jie feng ◽  
pengxu huang ◽  
wei wang

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Shiqiang Cheng ◽  
Liyang Wei ◽  
Xuelan Ma ◽  
Jianfeng Shen ◽  
Jian Wang
Keyword(s):  

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.


2018 ◽  
Vol 138 (9) ◽  
pp. 1075-1081
Author(s):  
Yasuhide Kobayashi ◽  
Mitsuyuki Saito ◽  
Yuki Amimoto ◽  
Wataru Wakita

Sign in / Sign up

Export Citation Format

Share Document