Vehicle Model Training for Occlusion-Aware Knowledge Distillation
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Solution Overview
Problem
Existing knowledge distillation techniques in autonomous vehicle systems fail to adequately address occlusions and uncertainties caused by sensor placements, leading to inconsistencies and inaccuracies in multi-modal data fusion, which can compromise the safety and reliability of autonomous driving systems.
Innovation Solution
Implement occlusion-aware and uncertainty-aware knowledge distillation methods that adjust features based on ground truth data and uncertainty measures to improve the accuracy and reliability of multi-modal sensor data fusion, reducing the need for extensive sensor calibration and enabling more efficient camera-only models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing knowledge distillation techniques are used for multi-modal data fusion, then the system complexity is reduced, but the accuracy and reliability deteriorate due to occlusions and uncertainties
Solution Approach 1:
The patent applies preliminary action by determining occluded regions and uncertainty measures before performing knowledge distillation. The system identifies occluded regions in image data and calculates uncertainty measures for initial feature data in advance, then uses this information to adjust the knowledge distillation process. This preliminary analysis enables the system to handle occlusions and uncertainties proactively, improving the reliability of multi-modal data fusion without requiring complex real-time adjustments during inference.
Solution Approach 2:
The patent introduces uncertainty measures and occlusion awareness as intermediary elements between the teacher model and student model. These intermediaries serve as mediators that adjust the knowledge transfer process by weighting features based on their reliability. The uncertainty measures act as a bridge that allows the system to selectively trust or disregard certain features during knowledge distillation, thereby improving fusion accuracy without directly increasing system complexity.
2Measurement precision
If extensive sensor calibration is performed to address occlusions and uncertainties, then the measurement precision improves, but the time and resources required increase
Solution Approach 1:
The patent applies self-service by enabling the system to automatically determine occluded regions and calculate uncertainty measures without requiring external calibration processes. The teacher model generates initial feature data with embedded uncertainty information, and the system uses this self-generated information to adjust knowledge distillation. This self-service approach eliminates the need for time-consuming manual sensor calibration while maintaining measurement precision through automated uncertainty-aware processing.
Solution Approach 2:
The system performs preliminary determination of occluded regions and uncertainty measures during the training phase rather than requiring calibration during deployment. By pre-processing and identifying problematic regions in advance, the system eliminates the need for time-consuming calibration procedures while ensuring measurement precision is maintained through proactive identification and handling of occlusions and uncertainties.
3Productivity
If camera-only models are used to reduce computing resources, then the productivity increases, but the reliability decreases due to occlusions
Solution Approach 1:
The patent introduces uncertainty measures and occlusion awareness as intermediary mechanisms that enable camera-only models to compensate for the lack of multi-modal sensors. These intermediaries allow the camera model to identify and adjust for occlusions in the visual data, maintaining reliability despite using fewer sensors. The uncertainty measures act as a compensatory mechanism that preserves decision-making quality while reducing computing resources required for multi-modal processing.
Solution Approach 2:
The patent applies parameter changes by modifying the knowledge distillation process to incorporate uncertainty measures and occlusion awareness. By changing the parameters of feature selection and weighting based on uncertainty values, the system enables camera-only models to achieve reliable performance. This parameter adjustment allows the simplified camera-only architecture to maintain high reliability by dynamically adapting to occluded regions without requiring the computational resources of complex multi-modal systems.
Data Source
AI summary
This disclosure provides systems, methods, and devices for machine learning techniques for improved training, such as for vehicle surroundings analysis. In one aspect, a method is provided that includes receiving image data and position data from the area surrounding a vehicle, determining initial feature data based on the received data, and determining updated feature data for training a first model. The updated feature data may be determined based on uncertainty measures for portions of the initial feature data, occluded regions within the initial feature data, or combinations thereof. In certain aspects, the first model may be trained using knowledge distillation techniques. Other aspects and features are also claimed and described.