Vehicle Transfer Learning Using Experience Filters for Scenario Accuracy
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating diverse operational scenarios due to the need for extensive coding of every possible scenario, which is time-consuming and impractical, and transfer learning may not always provide optimal responses across different environments.
Innovation Solution
The use of transfer learning with an experience filter to generate a model based on state-action history entries, where similar scenarios are grouped and parameterized to represent probabilities for similar operational scenarios, allowing for efficient model generation and deployment in similar scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive coding is performed for every possible operational scenario, then model accuracy is improved, but development time and complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and storing state-action history entries from multiple operational scenarios in advance. These historical experiences are stored in a database and can be reused when encountering similar scenarios, eliminating the need to code every scenario from scratch and significantly reducing development time while maintaining model accuracy.
Solution Approach 2:
The system creates copies of learned experiences from similar operational scenarios by storing state-action history entries in a database. When a new scenario is encountered, the system retrieves and applies copied experiences from the database that are similar to the current scenario, reducing the need for extensive new coding while maintaining model accuracy through transfer learning.
2Productivity
If transfer learning is applied across different environments, then learning efficiency is improved, but response optimality may deteriorate
Solution Approach 1:
The system applies local quality by selectively retrieving state-action history entries that are locally similar to the current operational scenario based on feature comparison. Instead of applying generic transfer learning across all environments, the system identifies and applies only the locally relevant experiences from the database that match the specific characteristics of the current scenario, ensuring response optimality while maintaining learning efficiency.
3Adaptability or versatility
If all state-action history entries are used for model generation, then comprehensive coverage is improved, but processing complexity increases
Solution Approach 1:
The system extracts only the necessary state-action history entries from the database that are relevant to the current operational scenario. By comparing features of the current scenario with stored entries and selecting only similar matches, the system achieves comprehensive scenario coverage without processing all historical data, thereby reducing processing complexity while maintaining adaptability.
Data Source
AI summary
A group of state-action history entries may be determined from state-action history entries stored in a database. A state-action history entry may represent an experienced operational scenario. A state-action history entry may be associated with a feature. The group of state-action history entries may be determined based on a similarity of the feature. A parameter may be generated based on the group of state-action history entries. The parameter may represent a probability associated with experienced operational scenarios that are similar to one another. A model may be generated based on the parameter. The model may be configured for use in an operational scenario that is similar to the experienced operational scenarios when traversing a portion of the vehicle transportation network.