Interactive Cognition of Self-driving: A Multidimensional Analysis Model and Implementation

Self-driving vehicles rely closely on interactions with humans, vehicles, and the surrounding environment. However, the interactive analysis of self-driving is impacted by multiple perception sources, heterogeneous data, and complex environments in actual scenes. Due to the above issues, we are often unclear about the behavior of self-driving vehicles and do not understand their decisions, and it is also difficult to achieve synergy with our human intentions. We introduce the significance of research in the field of self-driving interactive cognition, detailing its components and underlying infrastructure. Furthermore, we demonstrate how self-driving interactive cognition, inspired by the Wiener model, embodies intelligence in complex environments with the purpose of stressing the importance of interactive cognition in complex environments and scientifically evaluating the analysis of machine interactive cognition. Then, a multidimensional analysis model of self-driving interactive cognition is established based on perceptual information acquisition, multichannel and cross-modal data registration, attention mechanism, visual recognition and understanding, and embodied dynamic control. Supported by the above, we build a multiview spatiotemporal graph convolutional network (MV-STGCN) model for action recognition to realize vehicle-to-human body language interactive cognition. Most importantly, we innovatively propose a nonlinear-CRITIC-technique for order preference by similarity to an ideal solution (TOPSIS)-based method to analyze the interactive cognition analyses of different action recognition algorithms efficiently, such as MV-STGCN. Future self-driving vehicles are bound to demonstrate multichannel and cross-modal intelligence perception and human-vehicle-friendly interaction, and we are committed to better realizing humanoid driving analysis and the embodied intelligence of self-driving vehicles. "Self-driving + interactive cognition" could make future vehicles become interactive wheeled robots that can be trusted and better serve human society.