Camera-Lidar Track Transfer for Wide-Range Vehicle Object Tracking
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Solution Overview
Problem
Existing object detection and tracking systems for autonomous vehicles face challenges in seamlessly transitioning object tracks between different sensing modalities, such as camera and lidar, leading to inaccuracies and reduced decision-making time, especially under adverse weather conditions.
Innovation Solution
A pipelined object tracking system utilizing multiple machine learning models processes data from various distance ranges, including camera, camera-lidar, and lidar models, to efficiently match and transfer object tracks, ensuring consistent and accurate tracking across modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object tracking uses separate camera and lidar models independently, then each model can be optimized for its specific sensing range, but track information becomes inconsistent when transitioning between modalities
Solution Approach 1:
The patent combines separate camera and lidar tracking models into a unified pipelined system where camera tracks and lidar tracks are merged and相互 informed. The camera model provides long-range tracking while the lidar model provides precise short-range tracking, and their results are integrated to maintain consistent object tracks across the full sensing range, preventing track information loss during modality transitions.
Solution Approach 2:
The patent introduces an intermediary mechanism that facilitates information exchange between the camera and lidar tracking models. This intermediary allows track hypotheses from the camera model to be validated and refined by the lidar model, and vice versa, ensuring smooth transitions and consistent track information across different sensing modalities without direct conflict between independent models.
2Adaptability or versatility
If the system processes object tracking data through multiple separate models, then comprehensive coverage is achieved, but processing time increases
Solution Approach 1:
The patent segments the object tracking process into distinct pipeline stages: camera-based long-range detection, transition zone processing, and lidar-based short-range precision tracking. Each segment handles specific distance ranges and requirements, allowing parallel processing and optimization of each stage independently while maintaining overall efficiency and comprehensive sensing coverage.
Solution Approach 2:
The patent performs preliminary object detection and track hypothesis generation using the camera model before engaging the computationally intensive lidar model. This preliminary action filters out objects that don't require lidar-level precision and prepares track hypotheses in advance, allowing the lidar model to focus only on critical transitions and refinements, thereby reducing overall processing time while maintaining comprehensive coverage.
3Device complexity
If traditional object detection is used without pipelined processing, then system complexity is lower, but tracking accuracy across wide distance ranges deteriorates
Solution Approach 1:
The patent implements a dynamic pipelined processing system that automatically adjusts the interaction between camera and lidar models based on object distance, speed, and track confidence levels. The system dynamically switches between camera-only tracking, lidar-only tracking, and combined tracking modes, optimizing tracking accuracy for different scenarios while managing system complexity through adaptive control rather than static complex architecture.
Data Source
AI summary
The described aspects and implementations enable efficient and seamless tracking of objects in vehicle environments using different sensing modalities across a wide range of distances. A perception system of a vehicle deploys an object tracking pipeline with a plurality of models that include a camera model trained to perform, using camera images, object tracking at distances exceeding a lidar sensing range, a lidar model trained to perform, using lidar images, object tracking at distances within the lidar sensing range, and a camera-lidar model trained to transfer, using the camera images and the lidar images, object tracking from the camera model to the lidar model.