Cross-Modal Trajectory Prediction for Complex Motion Interactions
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Solution Overview
Problem
Current trajectory forecasting methods for motion devices are limited by assumptions of slow interactive agent movement, open environments, lack of scene context consideration, and failure to generalize to various settings, as well as neglecting interactions with non-machine entities, which are crucial for safe automation system operation.
Innovation Solution
A computer-implemented method using a shared cross-modal latent space in a machine learning model to predict trajectories by integrating sensory data of different modalities, such as LiDAR and RGB images, to model motion behavior and generate control instructions for motion devices in complex environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If social interactions are validated from nearby surroundings with assumption of slow movement of interactive agent, then social interaction modeling is simplified, but the approach is not generally applicable to machines as they move faster
Solution Approach 1:
The system changes the parameter of interaction validation from spatial proximity alone to a combined temporal-spatial criterion, validating interactions based on whether agents are within a threshold distance for a minimum time duration. This allows the same model to handle both slow-moving social interactions and fast-moving machine interactions uniformly.
Solution Approach 2:
The trajectory prediction model is designed to be universal across different agent types and speed regimes. By using modality-agnostic feature extraction and a unified prediction framework that processes both image and point cloud data, the system can handle pedestrians, vehicles, and machines of various speeds with the same architecture.
2Device complexity
If the interactive environment is hypothesized as an open space, then the model structure is simplified, but there is no or minimal consideration of scene context which is not feasible in scenes with structured layouts
Solution Approach 1:
The system adds the dimension of scene context by incorporating point cloud data that represents the three-dimensional environment structure. This complementary modality provides spatial layout information that constrains and enriches trajectory predictions in structured environments like intersections and driveways, while maintaining compatibility with open space scenarios.
Solution Approach 2:
The point cloud data acts as an intermediary that bridges the gap between simplified open-space assumptions and complex structured layouts. By fusing image and point cloud features, the system indirectly captures scene context without requiring explicit scene graph construction or complex environmental modeling.
3Device complexity
If single modality sensory data is used, then data processing is simpler, but the solution is restricted to certain scenarios and not generalizable to other settings
Solution Approach 1:
The system merges multiple sensory modalities (RGB images and LiDAR point clouds) into a unified feature space through cross-modal fusion. The image encoder and point cloud encoder extract features independently, then combine them to form comprehensive scene representations that generalize across diverse scenarios including weather variations, lighting conditions, and different environment types.
4Device complexity
If interactions with non-machines are overlooked, then the model focuses on machine-agent interactions, but this is essential for the safe operation of automation systems
Solution Approach 1:
The system segments interaction modeling into distinct components: machine-to-machine interactions and machine-to-nonmachine interactions. By separately identifying and processing these interaction types with appropriate validation criteria for each, the system comprehensively models all relevant interactions while maintaining computational efficiency and safety.
Data Source
AI summary
Systems and methods for trajectory prediction and motion device control include receiving a first set of data about a surrounding environment of a motion device. The first set of data includes first sensory data and first position data. The method includes receiving a second set of data about the surrounding environment of the motion device. The second set of data includes second sensory data and second position data, where the first sensory data differs in modality from the second sensory data. The method also includes extracting features from the first set of data that model motion behavior of the motion device, and extracting features from the second set of data that model motion behavior of the motion device. The method includes inputting the features into a machine learning model, and computing a first trajectory based on a first latent variable and a second trajectory based on a second latent variable.