Summary
In vehicle‐driving simulation‐based communication systems, vehicles are always driven according to predefined driving styles. However, in the real world, various driving styles exist. To simulate various types of drivers in driving simulation systems, a new driving‐data generation method is required. This paper proposes a method that generates a realistic vehicle‐driving model. The data augmentation method is utilized to expand the driving dataset, and then the expanded driving data are clustered into several groups. The clustered driving data are inputted into a convolutional neural network to train a driving model. The driving model is utilized to classify another driving dataset into some categories. The driving data within the same categories are utilized to generate new driving data by combining the properties of the driving data. The new driving data thus generated is applied to a vehicle, which can be utilized in virtual driving simulation systems.
In this article, an application for object segmentation and tracking for intelligent vehicles is presented. The proposed object segmentation and tracking method is implemented by combining three stages in each frame. First, based on our previous research on a fast ground segmentation method, the present approach segments three-dimensional point clouds into ground and non-ground points. The ground segmentation is important for clustering each object in subsequent steps. From the non-ground parts, we continue to segment objects using a flood-fill algorithm in the second stage. Finally, object tracking is implemented to determine the same objects over time in the final stage. This stage is performed based on likelihood probability calculated using features of each object. Experimental results demonstrate that the proposed system shows effective, real-time performance.
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