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Automatic mining of vehicle behaviors from raw data collected by multiple sensors provides meaningful qualitative descriptions of the vehicle status. These qualitative behavior descriptions can be used in scenario parsing and have further applications in vehicle surveillance and frontal collision warning systems. In current approaches, the number of behavior categories is supposed to be known, or...
Currently, the prevalent frontal collision warning systems (FCWS) are mainly based on quantitative ways. Their warning algorithms usually do prediction and assessment in the quantitative level which can not supply a universal quality under different traffic scenarios. The lack of cognition to surroundings may probably mislead the threat assessment. Besides, it is not the quantitative method but qualitative...
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...
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