| With the requirement of automotive safety was getting stricter, more and more on-board sensors were used to form a system to detect environmental information. However, redundant and contradictive information from these sensors might cause the system ineffective. Thus we are trying to find an effective way to utilize the different sensors information, reduce the errors from every single sensor and form a comprehensive and exact result.Based on the fundamental principle of immunology, we designed an artificial immune model for solving the freeway-running vehicle's multi-sensor fusion problem, which called for a method to fuse the data from CCD camera and Radar sensor, wipe out the disturbance and redundancy, and obtained the important navigating information like the line, distance and velocity of frontal automotives.For specialty of the problem, we bring forward a two-phase fusion model of the artificial immune algorithm based on the clone selection theory. We set the sensors'pre-treated data as antigens and the fusion result as antibodies. As soon as the antigens invaded the system, it reacted to calculate the match between antigens and estimate if their information were accurate enough to directly recombine the gene together from both antigens then form an antibody. If not, we utilized the clone selection arithmetic to clone a new set of antibodies and calculate the fitness between antigens and them to decide a most suitable antibody. The diversity between the gene sectors is ensured by negative selection theory.We used MATLAB software to write a simulation program and set the samples of antigen. By simulating the different errors which would happen in the sensors'input data, and analyzing the outputs'error and misdetection ratio, we proved the validity of using the artificial immune arithmetic to solve the data fusion problem.In the end, we indicated the foreground of artificial immune theory in multi-sensor fusion area and the farther potential research directions.
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