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This paper proposes a real-time traveling path tracking algorithm for intelligent vehicle. Two encoders are installed on the left and right wheels to measure the rolling distances of the wheels. A virtual coordinate system fixed to the ground is constructed. The calculating algorithm of vehicle position and heading angle in the virtual coordinate system based on the recorded data from encoders is presented. The proposed algorithm is independent on external reference object or the global positioning system and unaffected by culverts, tall buildings, illumination, fog, rain etc. The experimental results show the efficiency of the proposed algorithm.

Unmanned cars' developing involves at least three major sections: environment perception, decision-making and subsequent vehicle control. Whereas the first and the latter have proven almost-solved problems, decision-making is considered to be the most important and difficult section in unmanned cars. In fact, no matter normal or unmanned cars, to ensure their safety, appropriate decision must be made timely by either human driver or computer. In this article, we present a decision-making method based on drivable area cutting. We considered the most common elements in actual driving process including traffic signs, traffic lights, pedestrians, vehicles and even other vehicles' lights and horns to cut drivable area. We name it safe area for this cut-drivable area. Then decision is made based on the safe area. Experiment results show that our method works well in the highway and can be used in different environment with little change.

Safety is the foremost quality to unmanned cars. In order to ensure the safety, unmanned cars must percept the surrounding environment precisely and exhaustively. To achieve this, various sensors including camera, lidar, and radar are equipped with unmanned cars. While the environment perception algorithms are relatively mature, there is no general solution for the multi-sensor information fusion for unmanned cars. In this article, we present a solution for multi-sensor information fusion for unmanned cars using radar map. With this solution, different environment information detected by various sensors can fuse naturally in radar map. Besides, the radar map is essentially a matrix and can be easily stored in memory. Experiment results show that radar map works well in all road conditions. And software development practices for unmanned cars also show that radar map can provide well support to decision-making, path planning and other subsequent sections.

In order to obtain obstacle information in the cross-county environment for an unmanned ground vehicle (UGV), AK-means clustering algorithm is applied in four-layer laser radar data mining in this paper. The result of clustering serves as candidate obstacles. To overcome the false clustering due to vibration of UGV, weighted Euclidean distance is used to improve Davies-Bouldin Index (DBI). The experimental results show that the proposed obstacle detection algorithm is reliable and robust in low speed driving.