Autonomous Vehicle Route Constraints for Dynamic Road Closures
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating through environments where map data is not updated to reflect changing travel conditions, such as road closures or events, leading to inefficiencies and potential safety hazards.
Innovation Solution
A system that allows autonomous vehicles to access and integrate navigational constraints, including exclusion and inclusion polygons, to dynamically adjust their routes based on real-time data from remote computing devices, ensuring compliance with up-to-date traffic flow information and event notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely on pre-stored map data for navigation, then the navigation system is simple and fast, but the map data cannot reflect changing travel conditions such as road closures or events
Solution Approach 1:
The system pre-stores map data and constraint data locally in the autonomous vehicle for fast access, while also establishing a communication link with remote computing devices that can send updated constraint information. This preliminary preparation allows the vehicle to operate with accurate data while maintaining the capability to receive updates, resolving the contradiction between data accuracy and system complexity.
Solution Approach 2:
The system implements a feedback mechanism where the autonomous vehicle receives updated constraint information from remote computing devices based on changing environmental conditions. The vehicle's navigation system continuously compares current travel conditions against stored constraint data and receives corrective updates, ensuring navigation accuracy without requiring complete map redesigns.
2Adaptability or versatility
If autonomous vehicles use updated map data to reflect changing conditions, then navigation accuracy improves, but the system requires complex real-time data updates and processing
Solution Approach 1:
The system extracts only the critical constraint information (geographic areas, travel way identifiers, and constraint types) from complete map updates and transmits only these essential elements to the autonomous vehicle. This extraction approach allows the vehicle to adapt to changing conditions without processing entire map datasets, reducing computational complexity while maintaining adaptability.
Solution Approach 2:
The navigation system divides the operational environment into discrete geographic areas and travel way segments, each with specific constraint attributes. This segmentation allows the system to process and update individual segments independently rather than handling entire maps, reducing processing complexity while maintaining comprehensive adaptability to local changes.
3Speed
If autonomous vehicles process constraint data locally, then navigation decisions are made quickly, but the vehicle requires significant onboard computing resources
Solution Approach 1:
The system creates simplified copies of constraint data in a compact format that can be stored locally and processed efficiently. Rather than storing and processing complete map datasets, the vehicle maintains condensed constraint information that captures essential navigation rules, enabling fast local decision-making with reduced computational and energy requirements.
Data Source
AI summary
An autonomous vehicle can access map data comprising travel ways within a surrounding environment of the autonomous vehicle, and further access constraint data that define original navigational constraints within the map data. The vehicle can further receive constraint files comprising additional navigational constraints within the map data and modify the constraint data based on the constraint files. The vehicle can determine a travel route to a destination using composite constraint data and autonomously driver to the destination along the travel route accordingly.