Scene and Image Embedding Fusion for Vehicle Object Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for detecting and classifying objects in a vehicle's environment often result in inaccurate and suboptimal outcomes due to sparse representations of sensor data, leading to missed important information and negatively impacting vehicle safety and efficiency.
Innovation Solution
A classification component combines scene and image embeddings to enhance object detection and classification by generating scene embeddings from sensor data and image embeddings from image capturing devices, then concatenating them for input into detection and classification models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is processed using traditional detection techniques, then the processing speed is maintained, but the detection accuracy and classification precision deteriorate due to sparse representations
Solution Approach 1:
The patent combines scene embeddings (from sensor data) and image embeddings (from image capturing devices) into a unified representation. This merging allows the system to leverage both the structured sensor data and the rich visual information from images, thereby improving detection accuracy while reducing information loss that occurs when using sparse sensor data representations alone.
Solution Approach 2:
The patent introduces embeddings as an intermediary representation layer between raw sensor data and the detection/classification models. These embeddings capture essential features in a compressed yet informative format, serving as a bridge that preserves important environmental details while enabling efficient processing by downstream models.
2Reliability
If only sensor data is used for object detection, then the system complexity is reduced, but the reliability of detection results deteriorates due to missed important information
Solution Approach 1:
The system merges data from multiple sources (sensor devices and image capturing devices) into a unified detection framework. By combining scene embeddings and image embeddings, the system achieves more reliable detection results that leverage the complementary strengths of different data modalities, while managing complexity through efficient embedding representations.
Solution Approach 2:
The patent creates a multi-functional system where the same embedding architecture processes both sensor data and image data. This universal approach allows a single detection/classification model to operate on multiple data types, improving reliability through diverse input sources without proportionally increasing system complexity.
3Measurement precision
If traditional detection models are used, then the processing time is reduced, but the classification precision deteriorates due to sparse sensor data representations
Solution Approach 1:
The patent performs preliminary processing by generating scene embeddings and image embeddings before feeding data into the detection/classification models. This pre-processing step extracts and compresses essential features in advance, allowing the main model to focus on high-level decision-making, thereby improving classification precision without excessive processing time penalty.
Solution Approach 2:
The patent transforms raw sensor data and image data into embedding representations, changing the parameter space from high-dimensional raw data to compressed feature vectors. This parameter transformation preserves critical information while reducing dimensionality, enabling precise classification with efficient processing suitable for real-time autonomous vehicle operation.
Data Source
AI summary
Techniques for detecting and/or classifying objects in a driving environment are described herein. A vehicle may receive sensor data and input the sensor data into a perception and/or localization component which may be trained to provide processed output data (e.g., bounding boxes, track(s), etc.). In such cases, the vehicle may generate a scene embedding based on the output data. Further, the vehicle may receive image data from an image capturing device on the vehicle. The vehicle may generate an image embedding associated with the image data and concatenate the image embedding with the scene embedding. The vehicle may input the concatenated embedding into a detection and/or classification model to detect and/or classify object(s) within the environment.