Latent Scene Modeling for Consistent Multi-Actor Motion Forecasts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing motion forecasting systems for autonomous vehicles struggle to accurately characterize joint distributions over future trajectories of multiple actors in complex scenes, leading to inconsistent and inefficient forecasting due to independence assumptions and sequential sampling methods.
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, incorporating graph neural networks for efficient parallel sampling and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If independence assumptions and sequential sampling methods are used in motion forecasting systems, then device complexity is reduced, but measurement precision and reliability of joint distributions over future trajectories deteriorate
Solution Approach 1:
The patent introduces an implicit latent variable model as an intermediary between the input scene data and the output trajectory predictions. This latent variable model captures the joint distribution over future trajectories of multiple actors without requiring direct complex interactions between all actors, thus maintaining manageable system complexity while improving forecasting accuracy through the latent representation of scene dynamics.
2Device complexity
If sequential sampling methods are used, then device complexity is reduced, but productivity and efficiency of motion forecasting deteriorate due to compounding errors
Solution Approach 1:
The patent performs preliminary action by pre-computing the latent distribution over scene dynamics from the input scene data before generating individual trajectory samples. This allows multiple trajectory predictions to be generated in parallel from the same latent distribution, eliminating the sequential dependency that causes compounding errors and improving forecasting efficiency.
3Device complexity
If independence assumptions are made for multiple actors, then device complexity is reduced, but reliability of scene-consistent forecasts deteriorates
Solution Approach 1:
The patent merges the individual actor predictions into a unified joint distribution over all actor trajectories by sampling from a shared latent distribution. This combining approach ensures scene consistency across all actors' trajectories while keeping the individual actor models relatively simple, resolving the contradiction between model complexity and scene consistency.
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.