Autonomous Vehicle Event Scheduling for Occlusion-Resilient Image Collection
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Solution Overview
Problem
Autonomous vehicles face challenges in collecting image data due to issues like vision occlusion and non-functional cameras, especially during events such as traffic incidents, where existing technologies fail to effectively direct vehicles to collect data from relevant locations.
Innovation Solution
A centralized scheduling system that determines a unique travel schedule for autonomous vehicles using dynamic programming or greedy algorithms to direct them to event locations, ensuring data collection from multiple perspectives and compensating for camera failures by utilizing data from other vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely on their own cameras to collect image data, then they can independently detect their surroundings, but they fail to collect data when cameras are occluded or non-functional
Solution Approach 1:
The patent introduces a centralized scheduling system as an intermediary that coordinates multiple autonomous vehicles to collect image data. When one vehicle's camera fails or is occluded, the scheduling system redirects other vehicles to capture the required data, ensuring reliability through this mediating coordination layer.
Solution Approach 2:
The patent makes autonomous vehicles multi-functional by enabling them to serve both their primary navigation purpose and secondary data collection purpose. Vehicles can be dynamically reassigned to different event locations based on camera status, allowing the fleet to collectively compensate for individual camera failures while maintaining overall system functionality.
2Stability of the object's composition
If autonomous vehicles follow fixed predetermined routes, then they can maintain stable navigation, but they cannot efficiently respond to dynamic events requiring data collection
Solution Approach 1:
The patent implements dynamic route adjustment by allowing autonomous vehicles to deviate from their predetermined routes when the centralized scheduling system identifies events requiring data collection. The system continuously updates vehicle assignments based on real-time event information and camera status, creating a dynamic balance between stable navigation and responsive event coverage.
Solution Approach 2:
The patent applies preliminary action by having the centralized scheduling system pre-identify events and prepare optimized routes before vehicles arrive at event locations. The system calculates and communicates revised routes in advance, allowing vehicles to smoothly transition from predetermined to event-specific paths without disrupting overall navigation stability.
3Device complexity
If a single autonomous vehicle collects all image data, then data collection is simple to manage, but coverage is insufficient due to vision occlusion and limited perspectives
Solution Approach 1:
The patent segments the image data collection task across multiple autonomous vehicles instead of relying on a single vehicle. The centralized scheduling system divides the coverage requirement into multiple perspectives, assigning different vehicles to capture images from various locations and angles, thereby reducing vision occlusion issues and improving overall data coverage.
Solution Approach 2:
The patent merges data collection efforts by coordinating multiple autonomous vehicles to work together on the same event coverage objective. The centralized scheduling system combines the capabilities of multiple vehicles, consolidating their image data into a comprehensive dataset that exceeds what any single vehicle could capture alone.
4Productivity
If the centralized scheduling system uses complex optimization algorithms, then route efficiency is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent implements self-service by designing the centralized scheduling system to autonomously perform route optimization and vehicle assignment without requiring external intervention. The system automatically processes event information, evaluates camera status, calculates optimized routes, and communicates assignments back to vehicles, creating a self-contained optimization loop that balances efficiency with manageable complexity.
Data Source
AI summary
An event scheduling system for collecting image data related to one or more events by one or more autonomous vehicles includes a centralized scheduling system in wireless communication with one or more autonomous vehicles. Each autonomous vehicle collects the image data related to the one or more events while following a unique travel schedule. The centralized scheduling system executes instructions to determine the unique travel schedule for a specific autonomous vehicle, where the unique travel schedule directs the specific autonomous vehicle to the specific location of the filtered event to collect the image data.