LiDAR Vehicle Tracking via Point Cluster Segmentation
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Solution Overview
Problem
LiDAR-based vehicle tracking systems face challenges such as limited observation of vehicle parts from a distance, fractured point cloud responses due to occlusion, and high computational requirements, especially in driver assistance and autonomous vehicles.
Innovation Solution
A method for generating vehicle tracklets using LiDAR point cloud data, which involves preprocessing, segmentation, grouping, and tracking, utilizing previous frame tracking information to aid current frame clustering and reducing computational load by using grouped clusters instead of raw LiDAR responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR-based vehicle tracking is performed using raw point cloud data, then tracking precision can be maintained, but computational power requirements increase significantly
Solution Approach 1:
The patent segments the point cloud data into multiple point clusters before tracking processing. This segmentation reduces the computational complexity by dividing the large-scale raw data into smaller, manageable clusters that can be processed more efficiently while maintaining tracking precision through subsequent refinement operations.
Solution Approach 2:
The patent performs preliminary clustering and grouping operations on the point cloud data before the main tracking process. By pre-organizing the data into structured clusters with associated geometric parameters, the system reduces the computational burden during real-time tracking while preserving the necessary precision for accurate vehicle position estimation.
2Speed
If point cloud data is processed in real-time for vehicle tracking, then tracking speed improves, but measurement precision deteriorates due to limited observation and occlusion
Solution Approach 1:
The patent implements feedback mechanisms where tracking results from previous frames are used to guide current frame processing. The system uses historical tracking information to predict vehicle positions and adjusts the point cluster assignment accordingly, improving both speed by reducing reprocessing and precision by incorporating temporal consistency.
Solution Approach 2:
The patent transitions from processing raw 3D point cloud data to working with simplified 2D projections and geometric parameters of point clusters. This dimensional reduction enables faster processing while maintaining tracking accuracy by preserving the essential spatial relationships needed for vehicle position estimation.
3Measurement precision
If detailed point cloud processing is performed, then tracking accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential features and parameters from the detailed point cloud data that are necessary for accurate tracking. By identifying and extracting key geometric parameters from point clusters rather than processing all raw data points, the system reduces processing complexity while maintaining tracking accuracy.
Solution Approach 2:
The patent transforms the complex point cloud data into simplified parameter representations including cluster centers, dimensions, and orientations. This parameter transformation reduces device complexity by converting detailed spatial data into compact numerical representations that are easier to process while retaining the information needed for accurate vehicle tracking.
Data Source
AI summary
A method of LiDAR-based vehicle tracking for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps including taking point clusters that belong to vehicles in a single LiDAR frame as input, tracking each vehicle as a probabilistic distribution, assigning each tracked vehicle to a corresponding one of the point clusters, and recalculating the probabilistic distribution of each tracked vehicle.