Autonomy Map Segmentation with Adaptive Alert Limits
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles require highly detailed autonomy maps for real-time localization, but existing systems lack efficient methods to segment these maps into class areas with adaptive alert limits for varying driving environments, affecting safety and operational efficiency.
Innovation Solution
A computing system executes a map segmentation engine on autonomy maps to classify road network features into class areas, associating each area with parameters such as alert limits, using probability density functions, convolutional neural networks, and graph neural networks to identify and classify road features, and adjust operational settings accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomy maps are segmented into class areas with adaptive alert limits, then safety and operational efficiency are enhanced, but device complexity increases
Solution Approach 1:
The autonomy map is segmented into multiple class areas (e.g., urban, suburban, rural, freeway zones) based on road network features and density metrics. Each class area is assigned specific alert limits and operational parameters, allowing the system to adapt safety thresholds to the characteristics of each zone rather than using a single global threshold.
Solution Approach 2:
Different alert limits and operational parameters are applied locally to different class areas within the autonomy map. For example, urban areas with high pedestrian density receive stricter alert limits while freeway areas with lower density receive more relaxed limits, optimizing safety and efficiency for each local context.
2Measurement precision
If detailed road network features are classified and segmented, then measurement precision of vehicle localization is improved, but processing time increases
Solution Approach 1:
The system pre-processes and segments the autonomy map into class areas with classified road network features before vehicles operate in these zones. This preliminary segmentation includes identifying and categorizing features such as intersections, pedestrian zones, and road types, so that vehicles can quickly reference pre-computed class areas and alert limits during real-time operation without performing complex analysis on-the-fly.
3Adaptability or versatility
If adaptive alert limits are implemented for different class areas, then adaptability to varying driving environments is improved, but computational requirements increase
Solution Approach 1:
The system implements dynamic adjustment of alert limits based on the vehicle's current location within class areas. As vehicles transition between different zone types (e.g., from rural to urban areas), the alert limits are dynamically updated to match the characteristics of the new class area, enabling adaptability without requiring continuous complex environmental analysis.
Data Source
AI summary
A computing system can execute a map segmentation engine on an autonomy map of a road network, where the autonomy map is recorded by one or more vehicles operating throughout the road network. Based on executing the map segmentation engine on the autonomy map, the system can classify a set of road network features in the autonomy map to (i) segment the autonomy map into a plurality of class areas, and (ii) associate each respective class area of the plurality of class areas with one or more parameters that regulate a manner in which vehicles operate through the respective class area.