Contrastive Learning for Multi-Modal Driving Scenario Augmentation
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Solution Overview
Problem
Current tools and methodologies for simulating driving scenarios in autonomous or semi-autonomous vehicles are inadequate, as they fail to comprehensively capture the complexity of real-world driving scenarios, including various weather conditions, traffic conditions, and driving behaviors. Additionally, existing systems struggle to process and organize the vast amounts of navigation-related data generated by these vehicles.
Innovation Solution
A computing system that harnesses multi-modal sensor data and navigation-related data by fusing it a priori. This system uses encoder-decoder architectures, such as transformer-based systems, to create a unified multi-modal embedding space. It performs scenario annotation and mining, enabling enhanced training of semi-autonomous vehicle systems and rigorous validation and verification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based paradigms are used for scenario analysis, then implementation is straightforward, but the system becomes unwieldy and cannot handle complex scenarios
Solution Approach 1:
The patent replaces rule-based mechanical systems with neural network-based learning systems. The neural networks automatically learn driving scenarios and patterns from data, eliminating the need for manual rule creation while handling complex scenarios effectively.
Solution Approach 2:
The system transforms discrete rule-based parameters into continuous learning parameters through neural networks. This allows the system to adapt to complex scenarios by adjusting learned parameters rather than following fixed rules, resolving the contradiction between implementation simplicity and complex scenario handling.
2Device complexity
If focused models analyze single type of sensor data, then computational complexity is reduced, but cross-modal insights are missed
Solution Approach 1:
The patent merges multiple focused models that analyze different sensor data types into a unified multi-modal embedding space. This combination preserves cross-modal insights while managing computational complexity through efficient architecture design and shared processing components.
Solution Approach 2:
The system creates a universal multi-modal embedding space that can process multiple sensor data types (visual, audio, sensor data) through a single framework. This universal approach prevents information loss while avoiding the complexity of separate dedicated systems for each modality.
3Adaptability or versatility
If heuristic blending methods combine data from multiple sensors, then data integration is achieved, but resolution to capture nuances is insufficient
Solution Approach 1:
The patent replaces heuristic blending methods with neural network-based processing. The neural networks automatically learn how to combine sensor data with appropriate weighting and resolution, capturing nuanced details that fixed heuristic rules cannot achieve.
Solution Approach 2:
The system transforms multi-sensor data into a multi-dimensional embedding space where nuances are preserved through additional dimensional information. This dimensional transformation allows high-resolution capture of subtle场景 details while maintaining data integration.
4Manufacturing precision
If manual annotation processes are used, then data labeling is achieved, but training processes are slowed down
Solution Approach 1:
The system implements self-service through automated annotation using neural networks. The models automatically label and annotate sensor data without human intervention, maintaining high labeling quality while dramatically increasing training speed and productivity.
Solution Approach 2:
The system performs preliminary automated annotation before human review or direct training use. This preliminary action provides high-quality initial labels that accelerate the training process while maintaining precision through subsequent refinement if needed.
5Productivity
If current systems process navigation data, then data handling is performed, but the intricate multi-modal embedding space creation is beyond their capabilities
Solution Approach 1:
The patent merges multiple processing capabilities into a unified system that creates multi-modal embedding spaces. By combining neural network processing with multi-sensor input handling, the system achieves both high productivity in data processing and the adaptability to create intricate embedding spaces.
Data Source
AI summary
A system includes one or more processors that obtain a textual prompt, encode the textual prompt, and obtain candidate sequences of sensor data from different modalities, each sequence including a plurality of sequential frames, each of the candidate sequences being evaluated against the textual prompt based on a contrastive loss. The system encodes the candidate sequences of sensor data, embedding position information, within the encoded candidate sequences, indicating relative timestamps associated with each of the sequential frames of the sensor data, concatenates the encoded candidate sequences of sensor data, including the embedded position information, transforms the concatenated and encoded frames of sensor data to form transformed candidate sequences, determines a particular candidate sequence as a match between the transformed candidate sequences and the encoded textual prompt based on the contrastive loss; and generates a hierarchical structure that encapsulates navigation data of the particular candidate sequence