Scene Tokenization for Accurate Multi-Agent Motion Prediction
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Solution Overview
Problem
Existing systems face challenges in accurately predicting the motion of agents in complex environments due to the high-dimensional nature of sensor data and the loss of information in simplified representations.
Innovation Solution
A system that processes both perception outputs and high-dimensional sensor data to generate tokens for trajectory prediction, decomposing sensor data into scene elements and encoding them along with perception outputs into a small number of tokens for use by a decoder model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If high-dimensional sensor data is directly used for trajectory prediction, then information completeness is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments high-dimensional sensor data into discrete scene elements (e.g., vehicles, pedestrians, road structures) and represents each element with separate tokens. This segmentation allows the system to process complex environmental information in manageable units, reducing computational complexity while preserving essential information about each scene component.
Solution Approach 2:
The patent introduces tokens as an intermediary representation layer between raw sensor data and trajectory prediction. These tokens serve as compressed summaries of scene elements, enabling the neural network to work with reduced-dimensional data that retains key environmental information without requiring full high-dimensional input processing.
2Productivity
If simplified representations are used for trajectory prediction, then processing efficiency is improved, but information loss increases
Solution Approach 1:
By segmenting the environment into distinct scene elements and assigning dedicated tokens to each element type (e.g., separate tokens for vehicles, pedestrians, road features), the system maintains specialized information about each category while using compact representations. This prevents information loss that would occur with overly simplified unified representations.
Solution Approach 2:
The patent applies different token representations to different scene elements based on their specific characteristics and importance. Critical elements receive more detailed token representations while less critical elements use compressed representations, optimizing the balance between information retention and processing efficiency for each local region of the scene.
3Measurement precision
If extensive neural network training is performed, then prediction accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary encoding of sensor data into scene element tokens before feeding them to the trajectory prediction network. This pre-processing step organizes raw data into structured representations that are more amenable to learning, reducing the training burden on the neural network while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms the input data representation from high-dimensional raw sensor data to a parameterized token system with controlled dimensionality. This parameter change reduces the search space for neural network training, allowing the model to achieve comparable accuracy with fewer training iterations and less computational resource consumption.
Data Source
AI summary
Methods, systems, and apparatus for predicting future trajectories of agents in an environment. In one aspect, a system comprises one or more computers configured to receive sensor data of an environment having one or more agents. The system decomposes the sensor data into a plurality of scene elements in the environment, and the system generates multiple tokens including a respective token for each respective scene element of the multiple scene elements in the environment. The system processes the multiple tokens using a decoder model to generate a respective predicted trajectory for the one or more agents in the environment.