Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms.

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Training a deep neural network with a small amount of data is a challenging problem as it is vulnerable to overfitting. However, one of the practical difficulties that we often face is to collect many samples. Transfer learning is a cost-effective solution to this problem. By using the source model trained with a large-scale dataset, the target model can alleviate the overfitting originated from the lack of training data. Resorting to the ability of generalization of the source model, several methods proposed to use the source knowledge during the whole training procedure. However, this is likely to restrict the potential of the target model and some transferred knowledge from the source can interfere with the training procedure. For improving the generalization performance of the target model with a few training samples, we proposed a regularization method called sample-based regularization (SBR), which does not rely on the source's knowledge during training. With SBR, we suggested a new training framework for transfer learning. Experimental results showed that our framework outperformed existing methods in various configurations.

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