Context-Specific Driving Models Through Distributed Vehicle Learning
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Solution Overview
Problem
Current AI and ML models for autonomous driving face challenges in training due to privacy concerns and the need for large amounts of contextual data, which can be bandwidth-intensive and fail to account for diverse driving contexts, leading to inefficiencies and safety issues.
Innovation Solution
A distributed learning framework where vehicles contribute locally learned models to a roadside unit, allowing for context-specific training data aggregation and model sharing, optimizing processing power, memory, and bandwidth while ensuring data privacy, and enabling the development of accurate, location-specific autonomous driving algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized training with large amounts of sensory data is used, then model accuracy is improved, but privacy protection deteriorates and bandwidth consumption increases
Solution Approach 1:
The patent segments the centralized training process into distributed local training across multiple vehicles. Each vehicle trains its own model locally using its sensory data, preventing centralization of sensitive data while still enabling model sharing and collective learning through the distributed network.
Solution Approach 2:
The patent introduces an intermediary mechanism where only model parameters (not raw sensory data) are shared between vehicles. This intermediary approach allows knowledge transfer while maintaining privacy, as the shared models contain aggregated patterns rather than sensitive individual data.
2Measurement precision
If centralized training with contextual data is used, then model accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential model parameters from the training process and transmits them between vehicles, rather than transmitting complete contextual datasets. This extraction approach significantly reduces bandwidth consumption while preserving the ability to learn context-specific driving patterns.
Solution Approach 2:
The patent segments the training workload so that each vehicle performs local training independently, then shares only the resulting model updates. This segmentation eliminates the need for continuous large-scale data transmission while maintaining model accuracy through distributed learning.
3Adaptability or versatility
If context-specific training data is aggregated centrally, then model adaptability is improved, but privacy protection deteriorates
Solution Approach 1:
The patent implements local quality by allowing each vehicle to maintain its own context-specific model trained on local data, while enabling these local models to share learned patterns. This ensures each vehicle optimizes for its specific context while contributing to collective knowledge without centralizing sensitive data.
Solution Approach 2:
Each vehicle performs self-service by training its own model locally using its own sensory data, eliminating the need to send raw data to a central server. The vehicle independently adapts to its local context while participating in the distributed learning network.
Data Source
AI summary
A method of implementing distributed AI or ML learning for autonomous vehicles is disclosed. An AI or ML model specific to a location or a type of the location is generated at a vehicle. In response to a detection that the vehicle is within a proximity to a road side unit (RSU) associated with the location or the type of the location or the vehicle is within a proximity to an additional vehicle that is present or anticipated to be present at the location or the type of the location, causing an AI or ML model transmission to the additional vehicle or the RSU. Based on the causing the AI or ML model reception, causing deployment of an additional AI or ML model in the vehicle to optimize the vehicle for the location or the type of the location.