Understanding the behavior of human drivers and how they interact with other drivers is crucial to develop and improve the decision-making capabilities of connected and automated vehicles (CAVs). This allows CAVs to anticipate and proactively respond to the actions of other road users in a safe and efficient manner, especially in mixed-traffic environments. Most existing studies rely on neural networks to model such interaction implicitly, and very few studies attempt to interpret the interaction. Considering its interpretability and flexibility, the Granger causality (GC) framework is widely used to understand the relationships between different agents, as well as how these relationships change over time. In this paper, we integrate the knowledge of traffic and vehicle dynamics into the neural network to learn the Granger causality and explore vehicular interaction from multi-vehicle trajectories. The proposed algorithm has been validated using both the INTERACTION dataset and field data collected in Riverside, California. The results show that our algorithm is able to address three key questions regarding vehicular interaction: 1) whether the interactions exist between/among the vehicles; 2) when the interactions occur and terminate; and 3) how strong the interactions between/among vehicles are.
Exploring Vehicular Interaction from Trajectories Based on Granger Causality
2023-06-04
3094618 byte
Conference paper
Electronic Resource
English
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