Map-Projected Computer Vision for Vehicle Depth And Object Detection
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Solution Overview
Problem
Autonomous vehicles face challenges with inaccurate and inefficient computer vision operations for depth estimation and object detection, which can impact safe navigation and compliance with traffic regulations.
Innovation Solution
The techniques involve projecting map data into image data to determine a projection region for map objects, generating coordinate channels, and using these channels as input for a machine learning model to produce depth maps and bounding box feature data, enabling more accurate predictions of object positions and trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision operations are used for depth estimation and object detection, then the system is simpler to implement, but the accuracy and reliability of detection results deteriorate
Solution Approach 1:
The patent merges map data with image data to create enhanced model input data. By combining prior knowledge from maps (road layouts, traffic regulations, geographic features) with real-time image capture, the system achieves more accurate depth estimation and object detection than traditional computer vision alone, while managing complexity through integrated processing pipelines
2Reliability
If traditional computer vision operations are used for object detection, then the processing speed is faster, but the reliability of detection results deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing map data into coordinate channels and projecting map features into the image coordinate system before object detection. This preparatory work organizes spatial relationships and regulatory constraints in advance, enabling the machine learning model to make more reliable detections without requiring extensive real-time computation during actual object detection
3Measurement precision
If map data is projected into image data to enhance computer vision operations, then the accuracy of depth estimation improves, but the complexity of data processing increases
Solution Approach 1:
The patent introduces coordinate channels as an intermediary representation between map data and image data. These coordinate channels encode spatial relationships, depth information, and regulatory constraints in a format that bridges the two data types, enabling the machine learning model to access enhanced information without requiring complex direct integration of map and image coordinate systems
Data Source
AI summary
Techniques for performing computer vision operations for a vehicle using image data of an environment of the vehicle are described herein. In some cases, a vehicle (e.g., an autonomous vehicle) can determine a predicted position (including a predicted depth) and/or a predicted trajectory for an object in the vehicle environment based on data generated by projecting a map object described by the map data for a vehicle environment to image data of the vehicle environment.