LAUSR

Deep computation models (DCMs) are widely used in intelligent transportation systems (ITS), like driving behavior detection, intelligent parking navigation and real-time road condition detection. Due to the multi-source heterogeneous nature of big data of the ITS, it is difficult for traditional DCMs to learn effective multi-modal data features. Although, the DCMs in tensor space can efficiently represent multi-modal data, it further worsens the problem of model learning parameter explosion. In this paper, we propose a lightweight tensor DCM. The model compresses the redundant learning parameters of the model and reduces the consumption of computational resources while maintaining the learning characterization capability of the DCM in tensor space, thus making the network model more general and lightweight for deploying the DCM to smart cars and edge devices. The proposed lightweight tensor DCM is evaluated on several real datasets. The experimental results show that the number of learning parameters is massively compressed while keeping the performance of the network model almost constant, while also reducing the computational complexity and training time of the model.