Selective Sensor Fusion for Real-Time Vehicle Understanding
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Solution Overview
Problem
Autonomous vehicles face challenges in understanding diverse vehicle attributes and navigating complex environments due to computational complexity and inefficiencies in real-time sensor fusion, particularly when leveraging multiple sensor modalities.
Innovation Solution
Implementing a multi-task machine learning model with selective sensor fusion using cross attention neural networks for specific task groups, preprocessing sensor data to extract part features, and limiting fusion to a desired field of view to reduce computational complexity while enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor fusion is performed using multiple sensor modalities to improve vehicle understanding, then measurement precision and reliability are improved, but device complexity and computational overhead increase
Solution Approach 1:
The patent segments the sensor fusion process by dividing sensors into different modalities groups (e.g., imaging sensors, LIDAR, RADAR) and processes them through separate backbone networks before fusion. This segmentation reduces the complexity of processing all sensors uniformly while maintaining comprehensive vehicle understanding capabilities.
Solution Approach 2:
The patent introduces a new dimension of modality-specific feature extraction by processing different sensor modalities through separate backbone networks. This dimensional separation allows independent optimization of feature extraction for each sensor type while enabling comprehensive fusion at higher levels, thereby improving understanding accuracy without proportionally increasing overall system complexity.
2Measurement precision
If sensor fusion is performed across all sensor modalities to improve vehicle understanding, then measurement precision is improved, but processing time and productivity are reduced
Solution Approach 1:
The patent segments the vehicle understanding tasks into different task groups (e.g., vehicle type classification, attribute detection, signal light detection) and selectively applies sensor fusion only to task groups that benefit from multi-modal data. This selective fusion maintains high processing speed for tasks that don't require fusion while improving accuracy for tasks that do.
Solution Approach 2:
The patent applies sensor fusion partially rather than universally - specifically applying it to certain task groups where multi-modal information provides significant value (such as detecting vehicle attributes or signal lights), while using single-modal processing for other tasks. This partial application maintains real-time processing capability while improving precision where it matters most.
3Measurement precision
If cross attention neural networks are used for sensor fusion to improve feature alignment, then measurement precision is improved, but computational overhead and device complexity increase
Solution Approach 1:
The patent segments the application of cross-attention mechanisms by applying them only within specific modality groups (e.g., between different imaging sensors) rather than across all sensor types. This selective application reduces computational overhead while maintaining feature alignment accuracy where it provides the most benefit.
Solution Approach 2:
The patent applies cross-attention neural networks partially - specifically for feature alignment within certain modality groups where it significantly improves measurement precision, while using simpler fusion methods for other sensor combinations. This partial application balances computational complexity with alignment accuracy.
4Measurement precision
If preprocessing is performed on all sensor data to extract part features, then measurement precision is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent segments the preprocessing operation by applying part feature extraction only to specific sensors or sensor groups that require it for the current task, rather than uniformly preprocessing all sensor data. This selective preprocessing reduces overall processing time while maintaining precision where part-level features are critical.
Data Source
AI summary
Autonomous vehicles utilize perception and understanding of vehicles to predict behaviors of the vehicles, and to plan a trajectory. Understanding of attributes of vehicles may be improved through sensor fusion. Sensor fusion can be computationally expensive and may be difficult to implement in a real-time vehicle understanding system. To limit computational complexity while benefiting from machine learning across modalities, sensor fusion may be selectively implemented for a subset of task groups of a multi-task machine learning model. In some cases, part-based understanding may be implemented before fusion to limit the features being fused together to part features that are most salient for the task group. In addition, sensor data and features that may be fused together can be limited to sensor data and features within a desired field of view. A model that implements sensor fusion may be disabled for objects that are beyond a threshold distance.