Neural Network Object Location Prediction for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current prediction techniques for determining future states of entities in environments, such as pedestrians and vehicles, often rely on physics-based modeling or rules-of-the-road simulations, which may not accurately account for dynamic attributes and interactions, leading to inefficiencies in navigation and safety in autonomous systems.
Innovation Solution
The use of machine learning models, specifically neural networks, to predict the locations of objects based on their attributes and those of proximate objects, allowing for real-time adaptation and improved trajectory planning in autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based modeling or rules-of-the-road simulations are used to predict future states of entities, then the prediction system has a structured framework for simulation, but the accuracy of predicting future locations is insufficient due to inability to account for dynamic attributes and interactions
Solution Approach 1:
The patent transforms the prediction approach by changing from fixed physics-based parameters to dynamic learned parameters. Neural networks learn optimal parameters from historical data, allowing the system to adapt to varying conditions while maintaining prediction accuracy. The model adjusts parameters based on observed patterns in entity behavior and environmental interactions.
Solution Approach 2:
The patent replaces traditional mechanics-based physics modeling with data-driven machine learning models. Instead of relying on predetermined physical laws and rules, the system uses neural networks to learn complex patterns from data, substituting mechanical simulation with intelligent prediction that better captures real-world dynamics.
2Measurement precision
If machine learning models are used to predict locations based on dynamic attributes, then the adaptability and accuracy improve, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the prediction task into multiple specialized neural network components, each handling specific aspects of entity prediction. This modular architecture divides complex computational work into manageable segments, improving both training efficiency and inference speed while maintaining high prediction accuracy.
Solution Approach 2:
The system performs preliminary data processing and feature extraction before main prediction operations. Historical data is preprocessed and stored in optimized formats, allowing the neural networks to operate on prepared inputs rather than raw data, significantly reducing real-time computational complexity.
Data Source
AI summary
Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.