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Image-Based Structured Vehicle Behavior Analysis Inspired by Interactive Cognition
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AI
DOI：10.1109/TMM.2024.3386026.png)
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Abstract
En 中文
Vehicle behavior analysis has gradually developed by utilizing trajectories and motion features to characterize on-road behavior. However, the existing methods analyze the behavior of each vehicle individually, ignoring the interaction between vehicles. According to the theory of interactive cognition, vehicle-to-vehicle interaction is an indispensable feature for future autonomous driving, just as interaction is universally required for traditional driving. Therefore, we place the vehicle behavior analysis in the context of the vehicle interaction scene, where the self-vehicle should observe the behavior category and degree of the other-vehicle that is about to interact with itself, in order to predict whether the other-vehicle will pass through the intersection first or later, and then decide to pass through or wait. Inspired by the interactive cognition, we develop a general framework of Structured Vehicle Behavior Analysis (StruVBA) and derive a new model of Structured Fully Convolutional Networks (StruFCN). Moreover, both Intersection over Union (IoU) and False Negative Rate (FNR) are adopted to measure the similarity between the predicted behavior degree and the ground truth. Experimental results illustrate that the proposed method achieves higher prediction accuracy than most existing methods, while predicting vehicle behavior with richer visual meaning. In addition, it also provides an example of modeling the interaction between vehicles and a verification for interaction cognition theory as well.
Keywords:
Turning
Roads
Analytical models
Cognition
Trajectory
Junctions
Vehicular ad hoc networks
Structured vehicle behavior analysis
interactive cognition
structured fully convolutional networks
structured label
vehicle-to-vehicle interaction
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