LiDAR Object Classification With BeV-RV Fusion Under Compute Limits
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Solution Overview
Problem
Existing LiDAR systems face challenges in efficiently identifying and classifying objects in a vehicle's surroundings, requiring significant computational resources and often failing when a subset of neural networks is not functioning as expected.
Innovation Solution
The use of parallel training and deployment of separate view neural networks (BeV and RV) for LiDAR data processing, combined with spatial smoothing and data augmentation, to enhance object classification accuracy by fusing multiple views of LiDAR scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural networks are used for LiDAR data processing, then object classification accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The system divides the LiDAR data processing into multiple specialized neural networks, each handling specific view types (e.g., Bird's Eye View, Range View). This segmentation allows parallel processing of different data aspects, improving classification accuracy while enabling independent optimization of each network's computational efficiency.
Solution Approach 2:
The system implements a hierarchical processing approach where a subset of neural networks processes data at different levels of detail. Not all networks process all data equally - some perform coarse filtering while others perform detailed classification, reducing overall computational burden while maintaining accuracy.
2Reliability
If multiple neural networks are deployed for LiDAR processing, then object identification reliability is improved, but system complexity increases
Solution Approach 1:
The system separates concerns by creating specialized neural networks for different processing tasks (classification, detection, filtering). Each network has a specific function, making the overall system more manageable despite having multiple components. This modular architecture improves reliability through functional specialization while controlling complexity through clear separation of duties.
Solution Approach 2:
Multiple neural networks are combined in a unified processing pipeline where their outputs are integrated. The system merges results from different view-based networks and processing stages, achieving improved reliability through redundancy and cross-validation while presenting a unified interface that hides the underlying complexity.
3Measurement precision
If spatial smoothing and data augmentation are applied, then classification accuracy is improved, but processing time increases
Solution Approach 1:
Data augmentation techniques are applied during the training phase rather than during real-time inference. Spatial smoothing operations are pre-computed or approximated using learned patterns from training data. This preliminary processing improves classification accuracy while minimizing the time penalty during actual object detection operations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces computational requirements and improves object classification accuracy by leveraging multiple views and spatial relationships, even when individual networks fail, resulting in more precise object identification for autonomous vehicles.
Implementation Method 1
LiDAR is a technology that uses light to obtain data about physical objects in line of sight of a light emitter
Data Source
AI summary
Among other things, techniques are described for controlling, using a control circuit, motion of a vehicle based objects identified using LiDAR. For example, respective classes of points of a point cloud are determined, and based on the determined respective classes of the points of the point cloud, objects in the vicinity of the vehicle are identified.