Lane-changing feature extraction using multisensor integration

We propose a feature extraction method for lane changes of other traffic participants. According to previous research, over 90 % of car crashes are caused by human mistakes, and lane changes are the main factor. Therefore, if an intelligent system can predict a lane change and alarm a driver before another vehicle crosses the center line, this can contribute to reducing the accident rate. The main contribution of this work is to propose a feature extraction method using the multisensor system which consists of a position sensor and a laser scanner with line markings information. For a lane change prediction of other traffic participants, the most effective features are a lateral position and velocity with respect to a center line. We installed the sensor system to the primary vehicle and measured positions of other traffic participants while the primary vehicle drives on a highway. We extracted the features as the distance with respect to the center line and the lateral velocity of other vehicles using the measurement data. We confirmed that our feature extraction method has an enough accuracy for the lane change prediction.