Trajectory Prediction Neural Network for Autonomous Vehicles
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Solution Overview
Problem
Existing trajectory prediction methods in robotics and autonomous driving suffer from low accuracy and reliability due to inefficient feature extraction and handling of noisy data, which limits their application in real-time environments.
Innovation Solution
A trajectory prediction method and device that utilize a neural network to acquire and process current trajectory and map data in real-time, expressing them as high-dimensional data points to extract global scene features, which are then used to predict future trajectory points and their probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trajectory prediction methods are used, then the system is simple to implement, but the prediction accuracy and reliability are low
Solution Approach 1:
The patent transforms trajectory data from traditional 2D spatial coordinates to high-dimensional space by incorporating temporal dimensions and feature embeddings. This dimensional transformation enables the model to capture complex spatiotemporal patterns and relationships that are invisible in conventional representations, thereby significantly improving prediction accuracy while managing system complexity through structured feature engineering.
Solution Approach 2:
The patent applies parameter changes by transforming raw trajectory coordinates into high-dimensional feature spaces through embedding layers and feature extraction. This parameter transformation allows the system to capture non-linear relationships and temporal dependencies, improving prediction reliability without requiring proportionally increased system complexity.
2Measurement precision
If complex feature extraction is performed to improve accuracy, then prediction precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-processing trajectory data into high-dimensional feature representations and extracting spatial-temporal features before the actual prediction task. This advance preparation organizes the data in a way that accelerates subsequent processing while maintaining high prediction precision, effectively reducing real-time computational burden.
Solution Approach 2:
The patent segments the complex feature extraction process into distinct modules: spatial feature extraction, temporal feature extraction, and high-dimensional space transformation. This segmentation allows each module to be optimized independently, improving overall processing efficiency while maintaining prediction precision through specialized feature extraction for each aspect.
3Reliability
If high-dimensional data processing is implemented, then feature extraction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent deliberately transitions to high-dimensional space to capture complex relationships in trajectory data that cannot be represented in lower dimensions. This dimensional change improves feature extraction accuracy and prediction reliability by enabling the model to distinguish subtle patterns and relationships, while the structured approach to high-dimensional processing manages computational complexity.
Data Source
AI summary
Provided are a trajectory prediction method and device, a storage medium, and a computer program to avoid low accuracy and low reliability of a prediction result in conventional trajectory prediction methods. A trajectory prediction neural network acquires input current trajectory data and current map data of current environment when a moving subject moves in the current environment. The current trajectory data and the current map data are expressed as a current trajectory point set and a current map point set in a high-dimensional space. A global scene feature is extracted according to the current trajectory point set and the current map point set. The global scene feature has a trajectory feature and a map feature of the current environment. Multiple prediction trajectory point sets of the moving subject and a probability corresponding to each prediction trajectory point set are predicted and output according to the global scene feature.