Crossroad Dynamic Map Data Sharing for Autonomous Vehicles
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Solution Overview
Problem
Conventional self-driving cars have limited detecting coverage, which prevents them from effectively monitoring crossroad situations and making comprehensive navigation decisions, especially when objects are beyond their detection range.
Innovation Solution
A system and method for updating and sharing crossroad dynamic map data using an on-vehicle detecting device and a computing device, which detects and processes information to create a geodesic coordinate system-based map, incorporating estimated coordinate shifts and rotation transformations to merge host-vehicle and object data into a comprehensive map, shared across vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional self-driving cars use only their own detecting units, then the system complexity remains low, but the detecting coverage is limited to the region nearby the self-driving car
Solution Approach 1:
The patent merges detection data from multiple self-driving cars into a unified crossroad dynamic map. The computing device collects detection information from multiple vehicles, transforms their local coordinates to a unified geodesic coordinate system, and integrates them into comprehensive map data that provides broader coverage than any single vehicle could achieve alone.
2Area of stationary object
If the self-driving car uses detecting units with larger coverage, then the detecting coverage improves, but the use of energy increases
Solution Approach 1:
The patent segments the detection task across multiple vehicles instead of requiring one vehicle to have omnidirectional detection capability. Each vehicle uses its own detecting units to detect objects in its vicinity, and the computing device aggregates these segmented detection results to form a complete crossroad view, reducing the energy burden on individual vehicles.
3Measurement precision
If the self-driving car monitors all crossroad situations continuously, then the navigation decision accuracy improves, but the loss of time for data processing increases
Solution Approach 1:
The computing device performs preliminary actions by continuously collecting, transforming, and integrating detection information from multiple vehicles to pre-generate comprehensive crossroad dynamic map data. This preliminary processing allows self-driving cars to receive ready-to-use navigation information, reducing their local processing time and enabling faster real-time decisions.
Data Source
AI summary
A system and a method for updating and sharing crossroad dynamic map data are disclosed. The method includes steps of receiving a detection information outputted from an on-vehicle detecting device, wherein the detection information includes a host-vehicle absolute coordinate, a host-vehicle course, a host-vehicle speed, a relative speed between an object and the host vehicle, and an initial relative coordinate between the object and the host vehicle; respectively performing matching procedures to the host-vehicle absolute coordinate and the initial relative coordinate by respectively adding estimated coordinate shifts to obtain a matched host-vehicle absolute coordinate and a matched relative coordinate; performing a coordinate rotation transformation to the matched relative coordinate to obtain a matched transformed coordinate; merging the matched host-vehicle absolute coordinate and the matched transformed coordinate into crossroad-section map data to form crossroad dynamic map data; and sharing the crossroad dynamic map data.