Spatial-Temporal Sensing Adaptation for Intelligent Driving
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Solution Overview
Problem
Current intelligent driving systems face challenges in adapting to diverse regional and temporal traffic environments, leading to reduced performance and safety when operating outside trained areas, as existing methods require extensive annotated data and are not cost-effective or practical for real-world deployment.
Innovation Solution
An information processing method that utilizes region and time information to enhance the sensing system's adaptability by outputting sensed information based on predefined algorithms or neural network models, allowing the system to better handle scenarios with large spatial-temporal variations, thereby improving the performance of intelligent driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive annotated data is used for training, then the sensing system can be trained, but the cost and complexity increase significantly
Solution Approach 1:
The patent changes the parameter of spatial-temporal context by injecting region and time information into the neural network model. This allows the model to adapt to different environments without requiring extensive retraining with annotated data for each specific scenario, thereby maintaining reliability while reducing training complexity.
Solution Approach 2:
The patent performs preliminary action by pre-training the neural network model on general data and then adapting it to specific regions and times through parameter adjustment rather than complete retraining. This preliminary training provides a foundation that reduces the need for extensive annotated data in deployment scenarios.
2Reliability
If the sensing system is trained for specific regions, then performance improves in those regions, but adaptability to other regions decreases
Solution Approach 1:
The patent implements universality by designing a neural network model that can function across multiple regions and time periods. By injecting spatial-temporal parameters (region and time information) into the model, it achieves multi-functionality where a single model can adapt to different scenarios without requiring separate trained models for each region, thus maintaining both accuracy and adaptability.
Solution Approach 2:
The patent applies dynamics by making the model adaptable through parameter injection rather than fixed training. The region and time information dynamically adjust the model's behavior to match current environmental conditions, allowing the system to transition smoothly between different scenarios while maintaining sensing accuracy.
3Device complexity
If traditional sensing methods are used, then the system structure is simple, but the performance in diverse environments is poor
Solution Approach 1:
The patent introduces an intermediary mechanism by injecting region and time information as additional inputs to the neural network model. This intermediary spatial-temporal context acts as a mediator that bridges the gap between simple sensing data and complex environmental variations, enabling the model to achieve high performance in diverse environments without significantly increasing system complexity.
Data Source
AI summary
An information processing method includes obtaining, by a first apparatus, first information that includes environment information of a terminal in which the first apparatus is located; receiving second information from a second apparatus, where the second information indicates region information and/or time information of the terminal; and then outputting first sensed information for the terminal based on the first information and the second information.