Scene-Interaction Latent Modeling for Consistent Motion Forecasts
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Solution Overview
Problem
Current autonomous vehicle motion forecasting systems struggle to generate scene-consistent motion forecasts due to the complexity of interactions between traffic participants, often relying on independence assumptions that lead to inconsistent or inefficient sampling, particularly in complex urban environments.
Innovation Solution
The implementation of an implicit latent variable model (ILVM) that uses a scene interaction graph to encode and decode latent distributions, allowing for parallel sampling and generating socially consistent future trajectories without factorizing the joint distribution, thereby capturing all stochasticity in the generative process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If independence assumptions are used for motion forecasting, then computational complexity is reduced, but scene consistency and reliability of motion forecasts deteriorate
Solution Approach 1:
The system segments the motion forecasting problem by introducing latent variables that separate the modeling of individual actor trajectories from the modeling of their interactions. The latent variable model divides the joint distribution into conditional distributions, where each actor's future trajectory is predicted independently given the latent scene representation, thus reducing computational complexity while maintaining scene consistency through the shared latent context.
Solution Approach 2:
The latent variables serve as intermediaries that capture scene-level context and interactions without requiring direct computation between all actor pairs. By introducing this intermediate latent representation layer, the system achieves scene consistency through the mediator while avoiding the exponential complexity of modeling all direct interactions between actors.
2Productivity
If joint distribution factorization is applied, then sampling efficiency improves, but accuracy in capturing actor interactions deteriorates
Solution Approach 1:
The system applies local quality by making each conditional distribution p(z_i|z_{-i}, x) specific to individual actors while maintaining global scene consistency through the shared latent representation. Each actor's latent variables and trajectory predictions are locally optimized given the scene context, enabling efficient parallel sampling while accurately capturing local actor interactions through the conditional dependencies in the latent space.
3Reliability
If complex interaction models are used between traffic participants, then scene consistency improves, but computational complexity and device requirements increase
Solution Approach 1:
The system extracts scene-level interaction patterns into a separate latent representation layer, taking out the complex interaction modeling from the direct trajectory prediction process. By extracting interactions into latent variables z that are inferred from scene observations x, the system achieves scene consistency without requiring computationally intensive direct interaction models between all actor pairs.
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.