Automated Driving Model Training via Closed-Loop Policy Updates
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Solution Overview
Problem
Current automated driving research and development platforms face inefficiencies due to the separation of data collection, storage, processing, model training, and testing functions, leading to delayed data cleaning, annotation, and classification, which increases research and development costs and reduces user experience.
Innovation Solution
A closed-loop system for automated driving that collects data according to specific scenarios, generates and tests automated driving models, and updates the collection policy based on testing results, integrating data collection, storage, annotation, and model training for efficient and systematic data application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data collection, storage, processing, model training, and testing functions are separated in automated driving platforms, then each function can be independently optimized, but the overall research and development efficiency decreases due to delayed data cleaning, annotation, and classification
Solution Approach 1:
The patent merges previously separate functions (data collection, storage, processing, model training, and testing) into an integrated automated driving platform. This integration enables seamless data flow between modules, eliminates delays in data cleaning and annotation, and allows parallel processing of multiple tasks, thereby improving overall research and development efficiency while maintaining the ability to independently optimize each functional module.
Solution Approach 2:
The integrated platform is designed with multi-functional capabilities where a single system performs multiple functions: data collection from sensors, storage in databases, processing through cleaning and annotation, model training using machine learning algorithms, and testing through simulation environments. This universal platform serves as a comprehensive solution that addresses all R&D needs in automated driving development.
2Ease of manufacture
If data processing and model training are performed separately, then each process can be optimized independently, but the overall development cost increases and user experience deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where testing results from the integrated platform are used to refine and improve the automated driving models. The system continuously cycles through data collection, processing, model training, and testing, with each iteration informing and improving the next. This closed-loop feedback ensures high reliability and continuously improving user experience while allowing independent optimization of each process stage.
3Device complexity
If traditional separate-function platforms are used, then system complexity is reduced, but data collection and application efficiency decreases
Solution Approach 1:
The integrated platform is segmented into distinct functional modules: data collection module (sensors), data storage module (databases), data processing module (cleaning and annotation), model training module (machine learning), and testing module (simulation). Each module is independently designed and optimized, yet they work together seamlessly within the integrated system, achieving high data collection and application efficiency without excessive complexity.
Data Source
AI summary
A method and an apparatus for automated driving, a device, and a computer-readable storage medium are provided. The method for automated driving includes: obtaining data associated with a parameter and an external environment of a vehicle, the data being collected according to a collection policy for a target scenario for the vehicle; generating an automated driving model for the target scenario based on the obtained data; and updating the collection policy by testing the automated driving model.