Pavement Marking Change Detection for Live Tile Map Shipping
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating safely when pavement markings change, as they rely on outdated semantic maps, which can lead to unsafe route selection and potential collisions or delays.
Innovation Solution
A pavement marking detection system that utilizes sensors like cameras, LIDAR, and RADAR to detect changes in pavement markings, compares the detected markings with existing semantic map data, and updates the maps accordingly, providing updated information to the vehicle fleet to ensure safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely on pre-existing semantic maps for navigation, then route determination is efficient and straightforward, but the system becomes unreliable when pavement markings change and the map data becomes outdated
Solution Approach 1:
The system implements feedback by continuously monitoring pavement markings during vehicle operation and comparing detected markings against the semantic map. When discrepancies are detected, the system triggers map updates, creating a closed-loop feedback mechanism that ensures map data remains current and reliable for navigation decisions
Solution Approach 2:
The system performs preliminary actions by proactively detecting pavement marking changes before they cause navigation errors. By continuously comparing real-time sensor data with stored map data, the system identifies and processes map updates in advance, preventing the accumulation of outdated information that would compromise navigation safety
2Reliability
If the system continuously monitors and updates semantic maps in real-time, then navigation reliability improves, but computational load and system complexity increase
Solution Approach 1:
The system applies partial action by selectively updating only those portions of the semantic map that contain detected pavement marking changes, rather than processing and updating the entire map continuously. This approach maintains high map data accuracy while significantly reducing computational load and system complexity compared to full-map continuous processing
3Measurement precision
If the system uses multiple sensors (cameras, LIDAR, RADAR) for pavement marking detection, then detection accuracy improves, but device complexity and cost increase
Solution Approach 1:
The system merges multiple sensor types (cameras, LIDAR, RADAR) into an integrated pavement marking detection system that processes information from all sensors simultaneously. By combining the complementary strengths of each sensor type, the system achieves high detection accuracy while managing complexity through unified processing architecture and coordinated sensor operation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables efficient and effective detection of pavement marking changes, allowing for rapid and safe route determination, reducing the risk of collisions and delays by ensuring all autonomous vehicles have up-to-date traffic maps.
Implementation Method 1
A pavement marking detection system that utilizes sensors like cameras, LIDAR, and RADAR to detect changes in pavement markings
Implementation Method 2
A pavement marking detection system that utilizes sensors like cameras, LIDAR, and RADAR to detect changes in pavement markings
Data Source
AI summary
Systems, methods, and computer-readable media are provided for detecting a pavement marking around an autonomous vehicle, comparing the detected pavement marking with a pavement marking present in a semantic data map, determining whether a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map, and updating the semantic data map based on the determining of whether the change has occurred between the detected pavement marking and the pavement marking present in the semantic data map.