Qualitative Modeling of Vehicle Behavior for Scenario Parsing

In most present frontal collision warning systems (FCWS), the warning algorithm mainly depends on simple combination of linearly predicted vehicle-motion parameters. Such systems suffer from high false-alarm-rate due to the incapability of identifying different transportation scenarios. Scenario parsing deals with such problems by analyzing transportation scenarios and applying specific threat assessment algorithm to each scenario. In this paper, approaches of symbolization and pattern/rule data mining are presented. Real-data experiments demonstrate that the approaches are effective