Autonomous Vehicle Perception Adaptation via On-the-Fly Self-Labeling
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Solution Overview
Problem
Current computer-aided perception systems in autonomous driving face challenges such as sensor limitations, high costs of manual data labeling, and the inability to adapt on-the-fly to dynamic situations.
Innovation Solution
The system implements a method for in situ perception in autonomous vehicles, using a plurality of sensors to receive data, track objects, and automatically label them on-the-fly through cross modality or temporal validation, allowing for local adaptation of models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-manual labeling is used to generate training data, then labeling accuracy can be ensured, but the process becomes too slow and costly to adapt on-the-fly
Solution Approach 1:
The system performs self-labeling by automatically generating training data and labels through its own sensing and processing capabilities. The autonomous vehicle uses its sensors to collect data, automatically labels the data through processing algorithms, and uses this labeled data to retrain its perception models in real-time, eliminating the need for external manual labeling services.
Solution Approach 2:
The system performs preliminary data collection and labeling during normal operation before actual adaptation is needed. By continuously collecting sensor data and pre-labeling it as it accumulates, the system prepares training data in advance, making rapid adaptation possible when new situations are encountered without waiting for slow manual labeling processes.
2Adaptability or versatility
If traditional perception systems use fixed models, then system complexity is reduced, but the system cannot adapt to dynamic situations and different environments
Solution Approach 1:
The perception model transitions from a static, fixed structure to a dynamic, evolving system. The model continuously adapts its parameters and structure based on incoming sensor data and performance feedback, allowing it to adjust to different environments, weather conditions, and driving scenarios while maintaining manageable complexity through automated adaptation processes.
Solution Approach 2:
The system implements closed-loop feedback where perception performance is continuously monitored, and the results feed back into model retraining and adjustment. This feedback mechanism enables the system to learn from its own performance, automatically adjusting to improve accuracy in dynamic situations without requiring complex manual reconfiguration.
3Quantity of substance
If a fleet of vehicles collects diverse data, then the richness of training information increases, but traditional approaches cannot process and adapt from such large volumes of data
Solution Approach 1:
The fleet data processing is segmented into distributed local processing at each vehicle and centralized aggregation. Each vehicle independently processes and labels its own sensor data locally, then contributes refined training samples to the fleet-wide model updates. This segmentation enables parallel processing of large volumes of data across multiple vehicles simultaneously, maintaining high productivity while utilizing the full richness of fleet-wide diverse data.
Data Source
AI summary
The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.