# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 3 Background
### Use Case Scenario: An Autonomous Vehicle Architecture  
The integration of travel time predictions into various applications, particularly autonomous vehicle systems, highlights the practical utility of our proposed framework. To illustrate this, we examine the Embedded Higher-order Agent (E-HoA) architecture [31], which demonstrates how our model can significantly improve operational efficiency in dynamic environments.  
Autonomous vehicles signify a major advancement in transportation technology, providing benefits such as enhanced safety, improved operational efficiency, and reduced road congestion. These vehicles rely on a combination of sensors including cameras, LIDAR, radars, and GPS to perceive their surroundings. By utilizing advanced algorithms in machine learning, computer vision, and artificial intelligence, they process these data to make informed decisions. In this context, accurate travel time prediction is essential for their navigation and scheduling systems, allowing vehicles to optimize routes and schedules to minimize travel time.  
To address the need for dynamic and precise travel time predictions, we propose integrating our model into the E-HoA architecture [31]. This distributed, layered framework is specifically designed to enable autonomous vehicles to function effectively in uncertain and dynamic environments. It facilitates seamless interactions between vehicles, viewed as autonomous agents, and their surrounding spatiotemporal context, leading to more efficient problem-solving and decision-making processes.  
In a practical scenario involving urban deliveries with autonomous vehicles, the integration of our travel time prediction model within the E-HoA architecture becomes essential. Consider an autonomous delivery vehicle operating in a bustling urban environment, tasked with delivering multiple packages within strict time constraints. The E-HoA system leverages the predictive capabilities of our model in conjunction with path generation algorithms and real-time data integration to dynamically optimize route planning and scheduling. This integration allows the vehicle to continuously adapt its routes and schedules based on real-time traffic conditions, ensuring optimal efficiency and timely deliveries. The layered architecture of the E-HoA, which incorporates our learning approach, is illustrated in Figure 2, depicting the interplay of various processes that govern the vehicle's behavior.  
Within the E-HoA architecture, the symbolic layer plays a crucial role by managing weights to facilitate concurrently scheduling intention behaviors. The heart of this layer is the Mental process, which revolves around the concepts of Beliefs (B), Desires (D), and Intentions (I) [32]. The Mental process is focused on optimizing the agent's intentions by engaging the Planning process to determine the best execution plan (\(\sigma\), a sequence of actions \(a\)) derived from the weighted intentions (\(I\)). The Planning process calculates the optimal execution plan (\(P\)) by considering the specific action duration (\(\delta(a)\)) within a given spatiotemporal context. Once the optimal plan is computed, the Mental process instructs the Execution process to perform the actions defined by the plan in sequence (\(\sigma\)).  
The field layer serves as the concrete layer of the E-HoA architecture. Here, the Action process converts symbolic actions (\(a\)) from the Execution process into a finite set of implemented maneuvers that control the vehicle's operations. This layer also includes the Sensory process, which captures real-world physical values (\(C\)) from the vehicle and its environment.