To extend the uTRAND framework to handle more complex anomalies involving interactions between multiple agents or anomalies spanning multiple patches in the intersection, several enhancements can be implemented:
Multi-Agent Interaction Modeling: Introducing a mechanism to track and analyze interactions between multiple agents can provide a more comprehensive understanding of anomalous behaviors. This can involve extending the graph representation to include edges between agents, allowing the framework to capture collaborative or conflicting movements.
Graph-Based Anomaly Detection: By incorporating graph-based anomaly detection algorithms, anomalies that involve coordinated actions among multiple agents or anomalies that propagate through the intersection can be effectively identified. Graph anomaly detection techniques can help in detecting patterns that deviate from the learned normal behaviors.
Temporal Analysis: Implementing temporal analysis techniques can enable the framework to consider the sequence of events and interactions between agents over time. This can help in detecting anomalies that unfold gradually or involve a series of coordinated actions across different patches.
Dynamic Rule Formulation: Developing dynamic anomaly detection rules that adapt based on the context of the anomaly and the relationships between agents can enhance the framework's ability to classify complex anomalies. Rules can be formulated based on the temporal order of events, spatial proximity of agents, and the nature of interactions observed.
By incorporating these enhancements, the uTRAND framework can evolve to handle more intricate anomalies involving multiple agents and spanning across various patches in the intersection, thereby improving its overall anomaly detection and classification capabilities.