Abstract
The forward traffic environment cannot be fully perceived by a single sensor or multiple homogeneous sensors in the driving condition. Therefore, it is necessary to fuse the heterogeneous sensors to realize the mutual cooperation and compensation. Aiming at the advantages and disadvantages of different vehicle sensors, millimeter wave radar and monocular camera are selected as vehicle sensors to perceive the forward information. Firstly, the coordinate relationship between the sensors is calculated and sensor coordinates are mapped into the same vehicle coordinate system. Secondly, target information from the sensors are processed in parallel. Thirdly, in the case of time and space synchronization of two kinds of signals, the signals are processed by the matching algorithm of Global Nearest Neighbor (GNN) and the two matched targets are combined into one by the weighted average method. Finally, the unmatched targets and matched targets are tracked to determine the final state by Extended Kalman Filter(EKF) algorithm. Results in real car environment reveal that the fusion method can make up the deficiency of single sensor and improve the recognition rate of target.
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