Adaptive Motion State Estimation for Real-Time Target Tracking
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Solution Overview
Problem
Existing motion state estimation methods for target objects in intelligent driving systems are limited by fixed motion models, requiring cumbersome and time-consuming adjustments of noise matrices, which are not suitable for real-time applications and environments.
Innovation Solution
A method for motion state estimation that involves determining first distances between an ego vehicle and a target object at a plurality of historical moments, and a second determination module, which is configured for determining a plurality of groups of sample data, where the group of sample data include a first sample distance between an ego vehicle and a target object at a current moment, and a second determination module, which is configured for determining a second distance between the ego vehicle and the target object at a current moment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an explicitly constructed motion model with fixed motion mode is used for motion state estimation, then the estimation can be performed with a structured approach, but the application scenarios are limited and the noise matrix adjustment process is cumbersome and time-consuming
Solution Approach 1:
The patent transforms the fixed motion model into a dynamic adaptive model by introducing learnable parameters that can automatically adjust to different motion modes. The motion state estimation model is trained on diverse driving scenario data, enabling it to adapt to various application scenarios without manual noise matrix adjustment, thus resolving the contradiction between structured estimation and scenario flexibility
Solution Approach 2:
The patent changes the parameters of the motion model from fixed values to learnable parameters that are optimized during training. By learning optimal parameters from data across multiple scenarios, the model achieves both reliable estimation and broad adaptability without requiring manual parameter tuning for each scenario
2Measurement precision
If noise matrix adjustment is performed based on actual driving environment to determine Kalman gain, then the motion state estimation accuracy can be improved for specific scenarios, but the process is rather cumbersome and time-consuming
Solution Approach 1:
The patent performs preliminary action by pre-training the motion state estimation model on comprehensive driving scenario data before actual use. The model learns optimal parameter configurations for various scenarios in advance, so during real-time operation, it can directly apply the learned parameters without time-consuming adjustment, thus achieving both high accuracy and efficiency
Solution Approach 2:
The patent implements self-service by enabling the model to automatically adapt to different driving environments through its learned parameters. The system serves itself by selecting appropriate motion modes and parameters based on the current scenario without external intervention or manual tuning, eliminating the time-consuming noise matrix adjustment process while maintaining estimation accuracy
Data Source
AI summary
Disclosed are a method for motion state estimation, and a method for training a motion state estimation model, a storage medium and an electronic device, which relate to technical field of intelligent driving. The method includes: determining first distances between an ego vehicle and a target object at a plurality of historical moments and a second distance between the ego vehicle and the target object at a current moment; performing fitting processing on the first distances and the second distance; and processing, based on a motion state estimation model, motion states of the target object at the plurality of historical moments obtained by the performing fitting processing on, to obtain a first estimation motion state of the target object at the current moment and a second estimation motion state of the target object at a future moment.