Edge-Deployable Spatiotemporal Modeling Network for Vehicle Behavior Recognition

Vehicle behavior recognition is essential for autonomous vehicles to quickly perceive and respond to their driving environment. In this paper, an edge-deployable spatiotemporal modeling network for vehicle behavior recognition is developed. Firstly, an Efficient SpatioTemporal Modeling (ESTM) Block is designed to extract both long-term evolution features and short-term motion information. Secondly, a Channel-Enhanced Spatial Modeling (CESM) Block is developed to capture the interdependencies among channels in spatial modeling. The proposed network can effectively process video input from onboard cameras while minimizing computational parameters and FLOPs. The combination of the ESTM and CESM blocks can produce rich and effective features for vehicle behavior recognition on edge devices. The experimental results demonstrate the effectiveness of the proposed method in real-world driving scenarios.