Trajectory Generation Model-Based IMM Tracking for Safe Driving in Intersection Scenario
Author(s) -
Tingting Zhou,
Ming Li,
Xiaoming Mai,
Qi Wang,
Fang Liu,
Qingquan Li
Publication year - 2010
Publication title -
international journal of vehicular technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.182
H-Index - 18
eISSN - 1687-5710
pISSN - 1687-5702
DOI - 10.1155/2011/103696
Subject(s) - trajectory , intersection (aeronautics) , tracking (education) , computer science , set (abstract data type) , control theory (sociology) , tracking error , engineering , simulation , artificial intelligence , transport engineering , control (management) , psychology , pedagogy , physics , astronomy , programming language
Tracking the actions of vehicles at crossroads and planning safe trajectories will be an effective method to reduce the rate of traffic accident at intersections. It is to resolve the problem of the abrupt change because of the existence of drivers' voluntary choices. In this paper, we make approach of an improved IMM tracking method based on trajectory generation, abstracted by trajectory generation algorithm, to improve this situation. Because of the similarity between human-driving trajectory and programming trajectory which is generated by trajectory-generated algorithm, the improved IMM method performs well in tracking moving vehicles with some sudden changes of its movement. A set of data is collected for experiments when an object vehicle takes a sudden left turn in intersection scenario. To compare the experiment results between IMM method with trajectory generation model and the one without, tracking error of the former decreases by 75% in particular scenario
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