Faced with intricate traffic conditions, the single sensor has been unable to meet the safety requirements of autonomous vehicles. In the field of multi-target tracking, the number of targets detected by vision sensor is sometimes less than the current tracks while the number of targets detected by millimeter wave radar is more than the current tracks. Hence, a multi-sensor information fusion algorithm is presented by utilizing advantage of both vision sensor and millimeter wave radar. The multi-sensor fusion algorithm is based on distributed fusion strategy that each sensor processes its own measurements to generate tracks respectively. At First, vision sensor and radar are used to detect the target and to measure the range and the angle of the target. Then, each sensor generates tracks for multi-target tracking. Vision sensor uses Hungarian Algorithm for data correlation and uses Kalman Filter for target tracking in view of its characteristics. For Millimeter wave radar, Probability Data Association (PDA) Algorithm is proposed for the balance of computational complexity and tracking performance. Finally, track-to-track correlation which is the key problem is implemented to judge whether two tracks represent the same target. For target detection, the vision sensor has high accuracy at azimuth angle and low accuracy at range, while radar has medium accuracy at azimuth angle and very high accuracy at range. The detection properties of two sensors should be considered when generating a match matrix. Simulation based on real test data which was taken by a monocular camera and a 77GHz millimeter wave radar is performed in MATLAB. Simulation result indicates that the gate of match matrix has a great impact on fusion performance and computational speed.