TY - JOUR
T1 - Dangerous Behavior Recognition of Autonomous Vehicles at Intersection Based on Gaussian Mixture Model
AU - Li, Shaopeng
AU - Wu, Shaobin
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/6/15
Y1 - 2020/6/15
N2 - Urban road intersections are a typical complex traffic scene, and they are also prone to road traffic accidents. Aimed at the safety problems of road intersections, this paper proposes a model of vehicle dangerous behavior recognition based on Gaussian mixture model method, which provides an empirical basis for autonomous vehicle behavior decision at intersections. By analyzing the characteristics and rules of driving behavior at intersections, we select parameters like PET as input parameters of the model, and then construct a simulation model based on Beijing's Weigongcun intersection, which is used to obtain the dataset in intersection scene. The obtained data was subject to Kalman filtering and normalized as data input of a Gaussian mixture model. Dangerous scenes and general scenes at the intersection scene were screened out. The screening results were analyzed and evaluated, and proved to be reliable. The model could provide an empirical basis for the decision-making design of autonomous vehicles at intersection.
AB - Urban road intersections are a typical complex traffic scene, and they are also prone to road traffic accidents. Aimed at the safety problems of road intersections, this paper proposes a model of vehicle dangerous behavior recognition based on Gaussian mixture model method, which provides an empirical basis for autonomous vehicle behavior decision at intersections. By analyzing the characteristics and rules of driving behavior at intersections, we select parameters like PET as input parameters of the model, and then construct a simulation model based on Beijing's Weigongcun intersection, which is used to obtain the dataset in intersection scene. The obtained data was subject to Kalman filtering and normalized as data input of a Gaussian mixture model. Dangerous scenes and general scenes at the intersection scene were screened out. The screening results were analyzed and evaluated, and proved to be reliable. The model could provide an empirical basis for the decision-making design of autonomous vehicles at intersection.
UR - http://www.scopus.com/inward/record.url?scp=85087548289&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1550/3/032014
DO - 10.1088/1742-6596/1550/3/032014
M3 - Conference article
AN - SCOPUS:85087548289
SN - 1742-6588
VL - 1550
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 3
M1 - 032014
T2 - 2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020
Y2 - 21 February 2020 through 23 February 2020
ER -