Vehicle Sensor Data Stitching for Blind Spot Object Tracking
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Solution Overview
Problem
Cameras affixed to physical infrastructure have limited fields of view, resulting in blind spots in collected data.
Innovation Solution
A system that stitches sensor data from vehicles linked to spatial, temporal, or operator criteria, aggregating data from multiple vehicles to generate comprehensive graphical user interfaces that present object movement and allow coordinated vehicle actions based on detected stimuli.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If cameras are affixed to physical infrastructure, then installation is simple and cost-effective, but field of view is limited resulting in blind spots
Solution Approach 1:
The system divides the monitoring task into multiple segments by deploying sensors across multiple vehicles instead of relying on a single fixed camera. Each vehicle acts as an independent monitoring unit, collectively covering areas that would be blind spots for any single fixed camera position.
Solution Approach 2:
The system transitions from a static, two-dimensional fixed camera view to a dynamic, multi-dimensional monitoring approach. Sensors on moving vehicles provide coverage across multiple spatial dimensions and time, eliminating blind spots by approaching the monitored area from various angles and positions.
2Loss of information
If sensor data is collected from multiple vehicles, then coverage of blind spots is improved, but data processing and stitching complexity increases
Solution Approach 1:
The system introduces a centralized processing platform that acts as an intermediary between multiple vehicle sensors and the final output. This intermediary receives raw sensor data, performs timestamp-based synchronization, stitches together data from different vehicles, and presents unified information, thereby managing complexity centrally rather than distributed across vehicles.
Solution Approach 2:
The system uses timestamp parameters as the key for stitching and synchronizing data from multiple vehicles. By changing the approach to use time-based parameter matching rather than spatial coordination, the system simplifies the complexity of integrating data from moving sources with varying positions and orientations.
3Loss of information
If vehicles transmit sensor data continuously, then data availability is improved, but energy consumption and network bandwidth usage increase
Solution Approach 1:
Instead of continuous transmission, the system implements periodic or event-triggered data transmission. Sensors on vehicles transmit data at intervals or when specific conditions are met (such as detecting relevant activity), maintaining data availability while significantly reducing energy consumption and network bandwidth requirements compared to continuous streaming.
Data Source
AI summary
A system can identify, from a first sensor of a first vehicle in response to an activation by the first vehicle of a mode that initiates transmission from the first sensor, first data of movement of an object and a timestamp, identify, based on the timestamp and from a second sensor of a second vehicle in response to an activation by the first vehicle of the mode, second data of the movement of the object, link, in response to a determination that a location of the first vehicle and a location of the second vehicle each satisfy a location threshold, the first data with the second data to indicate the movement of the object relative to the location of the first vehicle and the location of the second vehicle, and provide a presentation of the movement.