Autonomous Vehicle Knowledge Sharing via Neural Network Transfer
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Solution Overview
Problem
Autonomous vehicles face limitations in adapting to diverse environments and situations due to their predetermined training, which requires extensive data collection and analysis, and they primarily rely on information from certified sources, leading to slow learning and underutilization of shared experiences.
Innovation Solution
Creating a 'network of networks' where vehicles can share and integrate knowledge from other vehicles, using techniques like transfer learning and generative adversary networks to enhance AI systems, allowing vehicles to learn from each other's experiences and adapt to new conditions quickly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles use predetermined training for multiple locations and situations, then the vehicle can operate with higher autonomy degree in various environments, but the training requires significant time and effort for data collection, analysis, and model training
Solution Approach 1:
The system performs preliminary training actions by collecting and training on diverse sensory data across multiple locations and situations before deployment. The neural network is pre-trained with extensive training corpora covering various environments, so when the vehicle encounters a new situation, it can quickly adapt using the pre-established knowledge base rather than starting from scratch
Solution Approach 2:
The system creates copies of trained neural network models and shares them across multiple vehicles through a server infrastructure. Instead of each vehicle independently collecting and training on all possible scenarios, vehicles can copy and utilize training data and models from other vehicles that have already experienced similar situations, dramatically reducing the time and computational resources needed
2Adaptability or versatility
If autonomous vehicles collect and analyze large training corpuses of sensory data to identify relevant elements in new locations, then the vehicle can improve its behavior in diverse situations, but the process requires significant computational resources and time
Solution Approach 1:
The system merges training efforts across multiple vehicles by collecting sensory data from various sources and consolidating it into a shared training corpus. The neural network training process combines data from multiple vehicles' experiences, allowing the system to learn from collective knowledge rather than individual vehicle limitations, thereby reducing the computational burden on any single vehicle
Solution Approach 2:
A server acts as an intermediary between vehicles, centralizing the data collection, processing, and model training functions. The server receives sensory data from multiple vehicles, performs the computationally intensive training operations, and distributes the trained models back to the vehicles. This intermediary approach eliminates the need for each vehicle to independently perform resource-intensive training operations
3Reliability
If autonomous vehicles rely on information from certified sources only, then the training data quality can be ensured, but the learning process becomes slow and experiences from other vehicles are underutilized
Solution Approach 1:
The system makes the trained neural network models and training data universally available to all vehicles through the server infrastructure. A single training corpus and model can serve multiple vehicles across different locations and situations, allowing each vehicle to benefit from the collective experiences of all vehicles in the network rather than being limited to certified sources only
Solution Approach 2:
The system implements feedback loops where vehicles continuously share their sensory data and performance outcomes with the server. The server uses this feedback to refine and retrain the neural network models, which are then redistributed to vehicles. This continuous feedback mechanism ensures data quality while accelerating learning through iterative improvements based on real-world performance
Data Source
AI summary
Methods, systems and computer program products for generating content for training a classifier, including: receiving two or more parts of a description, for each part, retrieving from an extracted feature collection library one or more extracted feature collections derived from one or more video frames, the extracted feature collections or the video frames labeled with a label associated with the part, thus obtaining a multiplicity of extracted feature collections, and combining the multiplicity of extracted feature collections to obtain a combined feature collection associated with the description, the combined feature collection to be used for training a classifier.