Travel Lane Marker Classification With Dynamic Embeddings
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Solution Overview
Problem
Existing assisted and autonomous driving systems face challenges in accurately detecting and classifying objects and travel lane features due to issues with false positives, scalability, and the need for extensive training data, particularly in handling edge cases and out-of-distribution objects.
Innovation Solution
A system utilizing a two-stage neural network approach, where a first stage detects objects or features and a second stage transforms them into representative embeddings, allowing for dynamic clustering and classification, with real-time modification of clusters to improve accuracy and reduce false positives without full network retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a two-stage neural network approach is used with dynamic clustering, then classification accuracy is improved and false positives are reduced, but device complexity increases
Solution Approach 1:
The system divides the object detection and classification task into two distinct stages: a first stage that detects objects or features and a second stage that transforms them into representative embeddings for clustering and classification. This segmentation allows each stage to specialize in specific functions, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system implements dynamic clustering where clusters can be modified in real-time without requiring full network retraining. New clusters can be created and existing clusters can be updated based on incoming data, allowing the system to adapt to edge cases and out-of-distribution objects dynamically, thereby improving classification accuracy without the complexity of complete model retraining.
2Reliability
If extensive training data is used to handle edge cases, then detection reliability is improved, but loss of time increases due to training requirements
Solution Approach 1:
The dynamic clustering mechanism allows the system to handle edge cases and out-of-distribution objects by creating and modifying clusters in real-time based on incoming data, rather than requiring extensive pre-training data. This dynamic adaptation enables the system to learn from new patterns as they appear, improving reliability without the time cost of comprehensive retraining.
Solution Approach 2:
The system performs partial training by updating only the necessary cluster representations rather than retraining the entire neural network. This partial action approach allows the system to incorporate new information and handle edge cases effectively while minimizing the time required for updates, as only specific cluster embeddings need to be adjusted rather than the complete model.
3Measurement precision
If full network retraining is performed to improve classification, then measurement precision is improved, but productivity decreases due to training time
Solution Approach 1:
Instead of performing full network retraining, the system applies partial training by updating only the necessary cluster embeddings and representations. This selective update approach maintains classification precision for edge cases and out-of-distribution objects while significantly reducing the time and computational resources required, thereby improving productivity.
Solution Approach 2:
The dynamic cluster modification capability allows the system to update its classification models in real-time based on new data patterns without requiring complete retraining. This dynamic adjustment maintains high measurement precision for diverse objects while avoiding the productivity loss associated with full network retraining cycles.
Data Source
AI summary
A method for travel lane element classification, including obtaining, via a processing circuit, information indicative of a travel lane including one or more travel lane elements located within an environment of a vehicle; generating a plurality of keypoints from the information; organizing the plurality of keypoints into one or more subgroups of keypoints, wherein each of the one or more subgroup of keypoints is indicative of one or more categories of travel lane elements; generating one or more embeddings of the one or more subgroup of keypoints, and classifying, based on the one or more embeddings, the one or more organized subgroup of keypoints as indicative of a travel lane marker. The classifying triggers a determination of a driving related operation to be executed by the vehicle.