Crowdsourced Dynamic Road Feature Detection for Vehicle Control
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Solution Overview
Problem
Existing systems struggle to effectively detect and manage dynamic road features such as variable speed limits and reversible lanes, which change over time, leading to potential inaccuracies in vehicle navigation and control.
Innovation Solution
A system and method for crowdsourced detection of dynamic road features using vehicle sensors, communication with a remote server, and conflict resolution algorithms to ensure accurate data aggregation and vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static road feature detection methods are used, then system complexity is low, but the system cannot accurately detect dynamic road features that change over time
Solution Approach 1:
The patent combines data from multiple vehicles (crowdsourcing) to detect and verify dynamic road features. By merging observations from numerous vehicles passing through the same location at different times, the system achieves high detection accuracy for dynamic features without requiring complex individual vehicle sensors or processing systems.
Solution Approach 2:
The system uses universal sensors already present in vehicles (cameras, LIDAR, GPS) for multiple purposes: detecting static road features, dynamic road features, and verifying changes over time. This multi-functional approach improves detection capability without adding dedicated complex hardware for dynamic feature detection.
2Reliability
If crowdsourced data from multiple vehicles is collected and verified, then detection reliability improves, but data processing time and communication overhead increase
Solution Approach 1:
The system performs preliminary filtering and validation of crowdsourced data at the vehicle level before transmission to the server. Each vehicle pre-processes its sensor data to identify potential dynamic road features, reducing the volume of raw data that needs centralized processing and enabling faster overall system response.
Solution Approach 2:
The system implements feedback mechanisms where detected dynamic road features are continuously verified by subsequent vehicle observations. When multiple vehicles confirm the same dynamic feature change, the system rapidly converges on reliable detection, reducing the time needed to achieve high confidence in检测结果.
3Measurement precision
If dynamic road features are detected and stored in a database, then vehicle navigation accuracy improves, but data storage and management complexity increase
Solution Approach 1:
The system stores road features dynamically, distinguishing between static features (permanent road markings) and dynamic features (changeable speed limits, reversible lanes). This dynamic data structure allows the navigation system to query only relevant features based on current conditions, reducing data management complexity while maintaining high navigation accuracy.
Solution Approach 2:
The patent segments road feature data into distinct categories (static features, dynamic features, temporal schedules). This segmentation allows the navigation system to efficiently manage and retrieve specific feature types as needed, reducing overall data management complexity while improving navigation precision through targeted data access.
Data Source
AI summary
A method for crowdsourced detection of a dynamic road feature includes detecting the dynamic road feature using a sensor of a vehicle and communicating with a dynamic road feature database of a remote server. Further, the method includes uploading a feature data to the dynamic road feature database of the remote server in response to detecting the dynamic road feature. The feature data includes information about the road dynamic feature previously detected by the sensor of the vehicle. Also, the method further includes controlling the movement of the vehicle using the feature data.