Trajectory Prediction Neural Network With Multimodal Scene Attention
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Solution Overview
Problem
Conventional systems struggle to effectively incorporate diverse multimodal inputs for trajectory prediction, leading to complex architectures that are difficult to scale, extend, or tune while preserving accuracy and efficiency, and often fail to generate a complete distribution of diverse possible trajectories.
Innovation Solution
A system that uses a scene encoder to fuse modalities across temporal and spatial dimensions and a trajectory decoder to cross-attend representations of these inputs, resulting in a simpler implementation that improves model quality, decreases latency, and increases accuracy of trajectory prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use complex architectures to incorporate diverse multimodal inputs, then trajectory prediction accuracy is improved, but device complexity increases and scalability decreases
Solution Approach 1:
The patent combines multiple modalities (agent states, map data, traffic light states) into a unified scene representation using a single scene encoder that processes all inputs together. This merging approach maintains prediction accuracy by preserving inter-modality relationships while avoiding the complexity of separate processing modules for each modality.
Solution Approach 2:
The scene encoder is designed as a universal component that handles multiple types of inputs (agent states, map data, traffic light states) through a single unified processing pathway. This multi-functional design eliminates the need for modality-specific modules, reducing architectural complexity while maintaining the ability to process diverse inputs effectively.
2Measurement precision
If conventional systems use complex architectures to incorporate diverse multimodal inputs, then trajectory prediction accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The architecture is segmented into distinct functional components: scene encoder for processing multimodal inputs, trajectory decoder for generating predictions, and loss function for training. This segmentation allows each component to be independently tuned and extended without affecting the entire system, improving ease of operation while maintaining accuracy.
Solution Approach 2:
The unified scene encoder serves as a universal processing module that can accommodate different modality types and configurations. This universality makes the system easier to extend to new modalities and tune for different scenarios without requiring complex architecture modifications.
3Productivity
If conventional systems fail to generate complete distribution of diverse trajectories, then computational efficiency is improved, but reliability deteriorates
Solution Approach 1:
The trajectory decoder dynamically generates multiple diverse trajectory predictions by processing the encoded scene representation through a neural network that outputs a distribution of possible future paths. This dynamic approach ensures complete coverage of potential trajectories while maintaining computational efficiency through efficient neural network operations.
Solution Approach 2:
The system uses a loss function that provides feedback during training to ensure the predicted trajectory distribution matches the ground truth distribution. This feedback mechanism guarantees reliability by continuously optimizing the completeness and accuracy of the predicted trajectory distribution while maintaining computational efficiency.
Data Source
AI summary
Methods, systems, and apparatus for generating trajectory predictions for one or more target agents. In one aspect, a system comprises one or more computers configured to obtain scene context data characterizing a scene in an environment at a current time point, where the scene includes multiple agents that include a target agent and one or more context agents, and the scene context data includes respective context data for each of multiple different modalities of context data. The one or more computers then generate an encoded representation of the scene in the environment that includes one or more embeddings and process the encoded representation of the scene context data using a decoder neural network to generate a trajectory prediction output for the target agent that predicts a future trajectory of the target after the current time point.