Latent Scene Modeling for Consistent Multi-Actor Motion Forecasting
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Solution Overview
Problem
Existing motion forecasting systems for autonomous vehicles struggle to accurately characterize joint distributions over future trajectories of multiple traffic participants, leading to inconsistent and inefficient motion planning, especially in complex urban environments, due to assumptions of independence and sequential sampling that result in compounding errors and low expressivity.
Innovation Solution
A computer-implemented method using an implicit latent variable model (ILVM) with a scene interaction graph and deterministic decoder to generate scene-consistent motion forecasts by encoding scene latent data into distributed latent variables, allowing parallel sampling and efficient generation of socially consistent future trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If independent assumptions and sequential sampling are used for motion forecasting, then device complexity is reduced, but manufacturing precision (forecasting accuracy) deteriorates due to compounding errors
Solution Approach 1:
The patent merges the forecasting of multiple traffic participants into a unified joint distribution model. Instead of independently forecasting each participant's trajectory and then combining results (sequential approach), the system jointly models all participants' future trajectories together, capturing their interdependencies and avoiding compounding errors from sequential sampling.
Solution Approach 2:
The patent introduces a latent variable model as an intermediary representation that captures the joint distribution of multiple traffic participants' trajectories. This latent space serves as a mediator that encodes scene context and interactions, allowing the system to sample consistent joint trajectories without directly modeling complex pairwise interactions between all participants.
2Manufacturing precision
If joint distributions over multiple trajectories are modeled accurately, then motion forecasting precision improves, but device complexity increases due to the need for scene interaction graphs and latent variable models
Solution Approach 1:
The patent segments the complex joint distribution modeling task into two parts: (1) a scene interaction graph that captures local interactions between traffic participants, and (2) a latent variable model that represents the global joint distribution. This segmentation allows the system to handle complexity in a modular fashion, processing local interactions separately while maintaining global consistency.
Solution Approach 2:
The patent transforms the complex multi-dimensional joint distribution problem into a lower-dimensional latent space representation. By encoding the joint distribution of multiple trajectories into a compact latent variable model, the system reduces the dimensional complexity while preserving the essential correlations and interactions between participants.
3Ease of operation
If sequential sampling is used for trajectory prediction, then ease of operation is improved, but productivity decreases due to compounding errors requiring multiple sampling iterations
Solution Approach 1:
The patent performs preliminary action by pre-computing the joint distribution model from historical trajectory data and scene context before actual forecasting is needed. The latent variable model and scene interaction graph are trained in advance, so that during runtime, the system only needs to sample from the pre-computed joint distribution, avoiding the need for iterative sequential sampling and reducing both complexity and computation time.
Data Source
AI summary
A computer-implemented method for determining scene-consistent motion forecasts from sensor data can include obtaining scene data including one or more actor features. The computer-implemented method can include providing the scene data to a latent prior model, the latent prior model configured to generate scene latent data in response to receipt of scene data, the scene latent data including one or more latent variables. The computer-implemented method can include obtaining the scene latent data from the latent prior model. The computer-implemented method can include sampling latent sample data from the scene latent data. The computer-implemented method can include providing the latent sample data to a decoder model, the decoder model configured to decode the latent sample data into a motion forecast including one or more predicted trajectories of the one or more actor features. The computer-implemented method can include receiving the motion forecast including one or more predicted trajectories of the one or more actor features from the decoder model.