Vehicle Data Prediction Model for Real-Time Ballistic Estimation
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Solution Overview
Problem
Autonomous driving vehicles face challenges in processing vehicle data quickly and accurately due to computational limitations, leading to delayed decision-making and potential errors from using less accurate, faster processing algorithms or costly high-performance computing units.
Innovation Solution
A system and method for ballistically estimating vehicle data using a prediction model that is adaptively updated based on the difference between calculated and estimated data, allowing for faster initial estimation and periodic model updates to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a powerful computing unit is used to process vehicle data, then the processing speed and accuracy are improved, but the system cost increases significantly
Solution Approach 1:
The system divides the computing task into two segments: a fast processing path using simplified algorithms for real-time decisions, and a slow processing path using comprehensive algorithms for model training and updates. This segmentation allows the system to achieve high accuracy without requiring a powerful computing unit for all operations, thereby reducing cost while maintaining precision.
Solution Approach 2:
The system performs preliminary actions by pre-training the prediction model offline using the slow processing path. Once trained, the model is deployed for real-time inference in the fast processing path, eliminating the need for complex real-time computations during critical decision-making moments, thus reducing the required computing power and cost.
2Speed
If a less accurate but faster processing algorithm is used, then the real-time decision-making capability is improved, but the computational performance and reliability deteriorate
Solution Approach 1:
The system implements feedback by continuously comparing the fast prediction results with the slow computed ground truth data. The differences are used to update and refine the prediction model parameters, ensuring that the fast algorithm maintains high reliability over time. This feedback loop allows the system to achieve both speed and accuracy.
Solution Approach 2:
The system changes parameters by dynamically adjusting the prediction model parameters based on the feedback from the slow processing path. When the environment changes or new patterns are detected, the model parameters are updated to reflect these changes, allowing the fast algorithm to maintain high accuracy without sacrificing speed.
3Speed
If prior knowledge is used to predict system behavior, then the processing speed is improved, but the system complexity and error propagation increase
Solution Approach 1:
The system creates a simplified copy of the complex slow processing algorithm in the form of a prediction model. This model copy captures the essential patterns and relationships learned from historical data, allowing fast predictions without replicating the full complexity of the original algorithm. The model serves as a lightweight surrogate that maintains predictive accuracy while reducing computational complexity.
4Speed
If prior knowledge is used for prediction, then the initial processing speed is improved, but the statistical performance and precision deteriorate
Solution Approach 1:
The system makes the prediction model dynamic by continuously updating its parameters based on feedback from the slow processing path. This dynamic adaptation allows the model to adjust to changing conditions and improve its precision over time, rather than relying on static prior knowledge that may become outdated. The model evolves to maintain high precision while preserving fast computation speed.
Data Source
AI summary
Embodiments of the disclosure provide systems and methods for ballistically estimating vehicle data. The system may include a communication interface configured to receive a first vehicle measurement taken at a first time point and a second vehicle measurement taken at a second time point. The system may further include at least one processor. The at least one processor may be configured to estimate a first version of vehicle data at a first speed for each of the second time point and a plurality of intermediate time points between the first time point and the second time point based on the first vehicle measurement using a prediction model. The at least one processor may be further configured to compute a second version of vehicle data at a second speed for the second time point based on the second vehicle measurement. The first speed is faster than the second speed. The at least one processor may also be configured to determine whether to update the prediction model based on a comparison between the first version of vehicle data and the second version of vehicle data for the second time point.