TY  - JOUR
PY  - 2021//
TI  - An interacting multiple model for trajectory prediction of intelligent vehicles in typical road traffic scenario
JO  - IEEE transactions on neural networks and learning systems
A1  - Gao, Hongbo
A1  - Qin, Yechen
A1  - Hu, Chuan
A1  - Liu, Yuchao
A1  - Li, Keqiang
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.<p />  <p>Language: en</p>
LA  - en
SN  - 2162-237X
UR  - http://dx.doi.org/10.1109/TNNLS.2021.3136866
ID  - ref1
ER  -