Lidar Frame Alignment for Faster Object Behavior Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting the behavior of objects due to uncertainties in lidar data processing, which is computationally expensive and resource-intensive, especially when processing multiple frames, affecting real-time decision-making.
Innovation Solution
A top-down segmentation and classification method is employed, aligning and reducing lidar data from multiple frames into a multichannel top-down representation, using channels for maximum and minimum heights and intensity, to efficiently process temporal data and reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple lidar frames are processed to improve object behavior prediction accuracy, then prediction reliability is improved, but computational resource consumption increases
Solution Approach 1:
The patent extracts and processes only the most relevant features from multiple lidar frames (e.g., maximum/minimum heights, intensity values, occupancy information) rather than processing complete point cloud data. This selective extraction reduces computational load while maintaining prediction accuracy by focusing on discriminative features for object behavior classification.
Solution Approach 2:
The patent segments the lidar processing task into distinct stages: data collection from multiple frames, feature extraction (height, intensity, occupancy), object detection, and behavior prediction. This segmentation allows efficient processing by handling each stage with optimized algorithms appropriate to its specific requirements.
2Reliability
If multiple lidar frames are processed to improve object behavior prediction accuracy, then prediction reliability is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary processing of lidar frames by pre-extracting key features (height values, intensity, occupancy) and organizing them into structured representations before object detection and behavior prediction. This preliminary organization of data accelerates subsequent processing stages by avoiding redundant computations on raw point cloud data.
Solution Approach 2:
The patent extracts only essential features from multiple lidar frames (maximum/minimum heights, intensity values, occupancy information) rather than processing complete point cloud data. This selective extraction reduces computational load and accelerates processing while maintaining prediction accuracy by focusing on discriminative features.
3Manufacturing precision
If complete lidar data is processed to improve segmentation accuracy, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and processes only the most relevant features from lidar data (maximum/minimum heights, intensity values, occupancy information) rather than processing complete point cloud data. This selective extraction reduces processing complexity while maintaining segmentation accuracy by focusing on discriminative features for object classification.
Solution Approach 2:
The patent applies different processing strategies to different regions of the lidar data based on their importance. For example, regions containing potential objects receive more detailed processing with multiple feature extractions, while empty regions are processed more efficiently. This local quality approach optimizes the balance between segmentation accuracy and processing complexity.
Data Source
AI summary
Techniques for detecting and classifying objects using lidar data are discussed herein. In some cases, the system may be configured to utilize a predetermined number of prior frames of lidar data to assist with detecting and classifying objects. In some implementations, the system may utilize a subset of the data associated with the prior lidar frames together with the full set of data associated with a current frame to detect and classify the objects.