Augmented Reality Traffic Data Collection with Mobile AR Devices
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Solution Overview
Problem
Conventional methods for collecting traffic movement data, such as manual observation and fixed electronic devices, are prone to human error, limited by fixed locations, and inefficient in resource utilization, failing to effectively capture data outside their field of view or during periods of inactivity.
Innovation Solution
The use of augmented reality devices equipped with video capture capabilities and spatial computing, allowing operators to move freely and collect data with human insights, processing video data to derive movement analytics, including classifications, locations, and speeds, while conserving computing resources and reducing installation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If fixed electronic devices are used to collect traffic data, then data collection is automated, but the devices are limited to fixed locations and cannot capture data outside their field of view
Solution Approach 1:
The patent transitions from fixed, stationary electronic devices to mobile computing devices that can move freely to different locations. Operators use handheld or wearable devices to collect traffic data dynamically throughout the study area, enabling the system to adapt to different locations and capture data from multiple perspectives rather than being constrained to predetermined fixed positions.
Solution Approach 2:
The mobile computing devices are equipped with autonomous capabilities including automatic video capture, machine learning-based object classification, and automated data processing. The devices independently identify and track traffic elements, vehicles, and pedestrians without requiring constant manual intervention, while simultaneously providing operators with augmented reality overlays and data feedback.
2Ease of operation
If manual observation is used to collect traffic data, then human insights can be applied, but human error reduces data accuracy
Solution Approach 1:
The patent merges the strengths of both manual observation and automated systems by combining human operators with machine learning algorithms. Operators provide contextual understanding and decision-making while the automated systems handle precise measurement, classification, and data recording. This hybrid approach leverages human insight for complex situation assessment while eliminating human error in data capture through automated video analysis.
Solution Approach 2:
The mobile computing device acts as an intermediary between the human operator and the traffic environment. It captures raw video data, processes it through machine learning models to identify and classify traffic elements, then presents processed information to the operator via augmented reality overlays. This intermediary system handles the precise measurement and classification tasks while the operator focuses on higher-level analysis and decision-making.
3Loss of information
If continuous data collection is performed, then comprehensive traffic information is captured, but computing resources are wasted during periods of inactivity
Solution Approach 1:
The system implements periodic or event-triggered data collection rather than continuous recording. The mobile computing device activates video capture and processing based on detected traffic activity, time-of-day patterns, or specific events of interest. During periods of low or no traffic activity, the device enters a low-power state, conserving computing resources while maintaining readiness to resume data collection when activity is detected.
Solution Approach 2:
The system applies partial action by selectively collecting and processing data based on predefined criteria such as traffic volume thresholds, time periods, or specific locations of interest. Rather than continuously analyzing all video feed, the machine learning models process only relevant frames or regions containing actual traffic elements, reducing unnecessary computing operations while maintaining comprehensive coverage of meaningful traffic patterns.
Data Source
AI summary
A movement analytics platform can generate instructions for collecting video data from a site that includes a roadway. The instructions can be provided to a device having augmented reality capabilities, wherein the instructions include content displayed by the device to indicate an area at the site that an operator of the device is to position within a field of view of a camera. A data feed received from the device can include video data corresponding to the area and contextual data to annotate the video data based on observations by the device operator. The data feed can be processed to derive movement analytics associated with the area at the site (e.g., classifications, locations, speeds, travel directions, and/or the like for one or more objects depicted in the video data). The device can be provided with additional augmented reality content based on the movement analytics.