Predicting Dynamic Map States from Limited Field-of-View Sensor Data

2026-02-12

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Representation Learning for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

When autonomous systems are deployed in real-world scenarios, sensors are often subject to limited field-of-view (FOV) constraints, either naturally through system design, or through unexpected occlusions or sensor failures. In conditions where a large FOV is unavailable, it is important to be able to infer information about the environment and predict the state of nearby surroundings based on available data to maintain safe and accurate operation. In this work, we explore the effectiveness of deep learning for dynamic map state prediction based on limited FOV time series data. We show that by representing dynamic sensor data in a simple single-image format that captures both spatial and temporal information, we can effectively use a wide variety of existing image-to-image learning models to predict map states with high accuracy in a diverse set of sensing scenarios.

Problem

• field-of-view
• dynamic map prediction
• sensor limitation
• environment perception
• autonomous systems

Innovation

• limited field-of-view
• dynamic map prediction
• spatio-temporal representation
• image-to-image learning
• deep learning