End-to-End Driving Neural Network Training for Edge-Case Validation
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Solution Overview
Problem
Current autonomous driving systems face challenges in scalability, safety, and cost efficiency due to the complexity and high cost of traditional methodologies, which require large volumes of expensive training data and rely on static, pre-built maps that are not adaptable to dynamic environments.
Innovation Solution
The development of a scalable end-to-end (E2E) neural network training and validation system using conditional imitation learning, which leverages a large dataset of operator-supervised driving data to fine-tune models for responding to difficult situations and edge cases, reducing the need for onboard computational power and historical data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional autonomous driving systems use aggregation of independent submodules with manual labeling, then system functionality is achieved, but development cost and data labeling expense increase significantly
Solution Approach 1:
The patent merges multiple independent submodules (perception, localization, mapping, path-planning) into a single end-to-end neural network model. This consolidation eliminates the need for separate manual labeling processes for each module and reduces overall development complexity while maintaining system functionality.
Solution Approach 2:
The patent uses simulation environments to generate synthetic training data that copies real-world driving scenarios. This approach replaces expensive manual labeling of real data with automated generation of training samples from simulated environments, significantly reducing development costs.
2Measurement precision
If traditional systems rely on pre-built high-definition maps, then localization accuracy is improved, but adaptability to dynamic environments deteriorates
Solution Approach 1:
The patent transitions from static pre-built maps to dynamic real-time environmental perception using neural networks. The system continuously adapts to changing environments by processing current sensor data through the end-to-end model, enabling both accurate localization and adaptability to dynamic conditions.
Solution Approach 2:
The patent replaces the mechanical process of building and updating high-definition maps with a neural network-based perception system. The end-to-end model directly processes sensor inputs to understand the environment, eliminating the need for separate map construction and updating processes.
3Reliability
If end-to-end neural networks are trained with large datasets, then model accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing and augmentation during the data preparation phase, creating diverse training samples in advance. This includes generating synthetic data from simulations and pre-processing real-world data to reduce the computational burden during actual model training, thereby reducing training time while maintaining accuracy.
4Measurement precision
If manual labeling of training data is performed, then supervised learning accuracy is improved, but labeling expense and time consumption increase
Solution Approach 1:
The patent uses simulation environments to generate synthetic training data that copies real-world driving scenarios. This automated generation process replaces expensive manual labeling while maintaining the quality and diversity needed for accurate supervised learning.
Solution Approach 2:
The system uses automated algorithms to generate and label training data from simulation environments without human intervention. The neural network itself participates in the labeling process through self-supervised learning techniques, eliminating the need for expensive manual annotation.
Data Source
AI summary
The technology disclosed comprises systems and methods for the training and validation for an end-to-end neural-network learning model configured for autonomous driving. The end-to-end neural-network learning model is trained using human-operated driving demonstration data to curate training data examples of driving tasks and driving routes, as well as curation of particularly difficult driving tasks. The determination of difficulty of driving tasks uses a combination of entropy measurements in training, evaluation of model performance, and manual labeling. The conditional imitation learning model can be configured as a memory-augmented transformer model that leverages a memory-cached frame buffer to access previous states in a driving trajectory. The disclosed technology can be applied to passenger vehicles or autonomous robots for delivery tasks.