Multimodal Sensor Fusion Training for Robust AV Perception
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Solution Overview
Problem
Autonomous vehicle navigation sensors face performance degradation due to adverse weather conditions and sensor errors such as miss-calibration, noise, and malfunctions.
Innovation Solution
The implementation of a multimodal perception system that combines inputs from multiple navigation sensors like cameras, LiDAR, and RADAR, and uses data augmentation techniques like noise injection and occlusion to improve robustness against adverse conditions during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If single sensor navigation systems are used, then device complexity is reduced, but reliability deteriorates under adverse weather conditions and sensor errors
Solution Approach 1:
The patent combines multiple navigation sensors (camera, LiDAR, RADAR) into a unified sensor system that captures multimodal data from different modalities. This merging of sensors allows the system to maintain reliable navigation performance under adverse weather conditions by leveraging the complementary strengths of each sensor type, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The sensor system is designed with multi-functionality to handle various adverse conditions (rain, fog, snow, darkness) using different sensors. Each sensor type serves multiple functions across different weather scenarios, allowing the system to maintain universal reliability across diverse environmental conditions without requiring separate specialized systems for each condition.
2Measurement precision
If standard training datasets are used, then training speed is improved, but measurement precision deteriorates due to lack of robustness against real-world adversities
Solution Approach 1:
The patent applies data augmentation techniques during the training data preparation phase to pre-inject noise, occlusions, and adverse weather conditions into training datasets. This preliminary action ensures that perception algorithms are trained on robust data that simulates real-world adversities, improving measurement precision without requiring additional training time for separate robustness training.
Solution Approach 2:
The system transforms standard training datasets by applying parameter changes such as adding noise, creating occlusions, and simulating adverse weather conditions. These parameter changes modify the training data characteristics to better reflect real-world conditions, improving perception accuracy while maintaining efficient training timelines through automated data transformation.
3Reliability
If perception algorithms are trained without noise injection, then ease of operation is improved, but reliability deteriorates under sensor errors and malfunctions
Solution Approach 1:
The patent converts the harmful effect of sensor noise and errors into a beneficial training mechanism by deliberately injecting noise and simulating sensor malfunctions during the training phase. This approach transforms what would normally be detrimental conditions into useful training scenarios that improve the algorithm's reliability and robustness against real sensor errors without complicating the operational deployment.
Data Source
AI summary
The automated driving perception systems described herein provide technical solutions for technical problems facing navigation sensors for autonomous vehicle navigation. These systems may be used to combine inputs from multiple navigation sensors to provide a multimodal perception system. These multimodal perception systems may augment raw data within a development framework to improve performance of object detection, classification, tracking, and sensor fusion under varying external conditions, such as adverse weather and light, as well as possible sensor errors or malfunctions like miss-calibration, noise, and dirty or faulty sensors. This augmentation may include injection of noise, occlusions, and misalignments from raw sensor data, and may include ground-truth labeling to match the augmented data. This augmentation provides improved robustness of the trained perception algorithms against calibration, noise, occlusion, and faults that may exist in real-world scenarios.