Occupancy Prediction System Using Intermediate Neural Embeddings
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Solution Overview
Problem
Existing occupancy prediction systems for autonomous vehicles rely on indirect training and are inefficient in generating occupancy outputs for multiple agents and flexible future time points, limiting their accuracy and computational efficiency.
Innovation Solution
A trained occupancy prediction system that processes scene data using an embedding neural network to generate intermediate outputs, allowing for direct prediction of occupancy probabilities for multiple surrounding agents and any requested future time point, with an occupancy output generation subsystem that shares and processes this intermediate representation efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing occupancy prediction systems use indirect training methods, then the system can be implemented with simpler training procedures, but the prediction accuracy is limited
Solution Approach 1:
The patent introduces an intermediate representation layer that acts as a mediator between the scene data input and the occupancy output. This intermediate representation captures essential spatial and temporal features, enabling more accurate occupancy predictions while keeping the overall system architecture manageable. The intermediate layer serves as a bridge that transforms complex sensor data into meaningful predictions.
Solution Approach 2:
The prediction system is segmented into distinct functional components: scene data processing, intermediate representation generation, and occupancy output generation. This segmentation allows each component to be optimized independently, improving overall accuracy while managing complexity through modular design.
2Adaptability or versatility
If the system generates occupancy outputs for multiple agents and flexible future time points, then the versatility and adaptability improve, but the computation time increases
Solution Approach 1:
The system performs preliminary processing of scene data to generate an intermediate representation that captures essential features for multiple time points. This preliminary action allows the system to efficiently answer occupancy queries for various future time points without reprocessing the entire scene data each time, significantly reducing computation time for flexible predictions.
Solution Approach 2:
The intermediate representation is designed to be universal and applicable to multiple future time points and multiple agents simultaneously. This multi-functional intermediate structure enables the system to generate occupancy predictions for any requested future time point without requiring separate processing pipelines, thereby maintaining versatility while improving computational efficiency.
3Measurement precision
If the system processes scene data through an embedding neural network to generate intermediate outputs, then the prediction accuracy improves, but the device complexity increases
Solution Approach 1:
The patent extracts the essential intermediate representation from the complex neural network processing and separates it from the occupancy output generation. This extraction allows the use of a powerful embedding neural network for feature extraction while keeping the occupancy prediction component simpler and more interpretable, thereby improving accuracy without proportionally increasing overall system complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining occupancies of surrounding agents. One of the methods includes obtaining scene data characterizing an environment at a current time point; processing a first network input generated from the scene data using a first neural network to generate an intermediate output; obtaining an identification of a future time point that is after the current time point; and generating, from the intermediate output and the future time point, an occupancy output, wherein the occupancy output comprises respective occupancy probabilities for each of a plurality of locations in the environment, wherein the respective occupancy probability for each location characterizes a likelihood that one or more agents will occupy the location at the future time point.