This paper proposes a shared-taxi scheduling algorithm based on the insertion heuristic for online taxi-hailing services. First, service quality and operation cost are considered in the optimization objective of the shared-taxi scheduling system. There are two scheduling strategies for request insertion. One is the minimum waiting and detouring time, and the other is the minimum detouring and idling costs. The second strategy also considers a weight factor between detouring and idling costs. Then, the framework of the shared-taxi scheduling algorithm is built based on the classic cheapest insertion heuristic. Finally, based on the artificial data of a large-scale road network and requests, three groups of experiments correspond to different numbers of taxis under the two scheduling strategies.
Accurate perception of lane lines position and lane width is a key factor in driver assistance systems. At the same time, night driving is an important driving scene. Therefore, this paper proposes a night-time lane lines positioning method based on camera and LiDAR fusion. The lane lines are detected in the image by using computer vision, and the road surface equation is established based on the deep fusion of image and point cloud data. Combining these two aspects, the position of the lane lines in the 3D space can be solved. According to the positioning results, the distance between the vehicle and the lane lines and the lane width can be obtained. After testing the actual collected data sets, the method can achieve 99% accuracy after testing, and the maximum error is within 3%. The proposed method integrates the image and point cloud information, completes the road surface point cloud and uses the road surface as the limiting condition of lane lines positioning to make the positioning result more accurate. The method can bring positive effect to traffic scene perception under night driving.
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