Research on prediction method of driver's lane changing behavior based on simulated driving

2021 ◽  
Author(s):  
junzhe lu ◽  
jiangtian li ◽  
jie feng ◽  
pengxu huang ◽  
wei wang
Keyword(s):  
Prediction Method ◽  
Lane Changing ◽  
Simulated Driving ◽  
Behavior Based

Forecasting Freeway On-Ramp Lane-Changing Behavior Based on GRU

2021 ◽  
Vol 147 (12) ◽  
Author(s):  
Jieming Cui ◽  
Guizhen Yu ◽  
Bin Zhou ◽  
Qiujun Liu ◽  
Zhengguo Guan
Keyword(s):  
Lane Changing ◽  
Behavior Based

Study on Vehicle Lane-Changing Behavior Based on Cellular Automaton

2018 ◽  
Vol 12 (1) ◽  
pp. 75-80
Author(s):  
Juan Li ◽  
Da-yi Qu ◽  
Cong Liu ◽  
Jin-zhan Wang ◽  
Xiang-hua Xu
Keyword(s):  
Cellular Automaton ◽  
Lane Changing ◽  
Behavior Based

scholarly journals Lane-change prediction method for adaptive cruise control system with hidden Markov model

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401880293 ◽  
Author(s):  
Wei Yuan ◽  
Zhen Li ◽  
Chang Wang
Keyword(s):  
Control System ◽  
Markov Model ◽  
Adaptive Cruise Control ◽  
Time Window ◽  
Hidden Markov ◽  
Prediction Method ◽  
Window Length ◽  
Cruise Control ◽  
Lane Changing ◽  
Cruise Control System

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.

A Driving-Behavior-Based SoC Prediction Method for Light Urban Vehicles Powered by Supercapacitors

2020 ◽  
Vol 21 (5) ◽  
pp. 2090-2099
Author(s):  
Houlian Wang ◽  
Gongbo Zhou ◽  
Rui Xue ◽  
Yuanjie Lu ◽  
Julie A. McCann
Keyword(s):  
Prediction Method ◽  
Driving Behavior ◽  
Behavior Based
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