An effective variable selection algorithm for Aggressive/Calm Driving detection via CAN bus

In this research, the aim is to come up with an algorithm determining most appropriate variables of CAN (Controller Area Network) bus data for Aggressive/Calm Driving detection problem. This study assists drivers to take attention their Aggressive/Calm Driving habits on steering wheel. System complexity increases as involving all the variables in the problem. Therefore we can get cost efficiency by eliminating variables. With this aim, the proposed algorithm is applied to find optimal variables before identifying driving mood. As an initial phase, we have realized several test-drives having employed drivers with different driving styles being aggressive and calm in order for collecting data needed. Afterwards the novel algorithm developed is applied to eliminate trivial variables. Proposed method is based on exploiting similar correlation characteristics related to variables appearing in both Aggressive and Calm driving. As applying the selection algorithm, similar relation clusters are obtained with the aim of searching for redundant variables that will be eliminated. In this manner we reach a favorable set belonging to optimal variables. This novel algorithm can be easily applied for the systems including binary data set.