Scene-Centric Trajectory Prediction With Low-Latency Agent Decoding
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Solution Overview
Problem
Existing trajectory prediction models for autonomous vehicles, particularly those using agent-centric neural networks, face challenges in computational efficiency and latency, making it difficult to generate high-quality predictions within the required time frame.
Innovation Solution
A trajectory prediction model that incorporates a scene-centric encoder neural network, a fusion neural network, and an agent-centric decoder, allowing for a single encoded representation of all agents in the scene, which is then refined using agent-specific features to generate accurate trajectory predictions with reduced latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agent-centric neural networks are used for trajectory prediction, then prediction accuracy is improved, but computational cost and latency increase significantly
Solution Approach 1:
The model segments the prediction task into two distinct phases: a shared scene encoding phase that processes global context once, and individual agent decoding phases that generate predictions for each agent. This segmentation allows the computationally intensive encoding to be performed only once per scene rather than once per agent, reducing overall computational cost and latency while preserving prediction accuracy through the specialized decoding stage for each agent.
Solution Approach 2:
The model merges the processing of multiple agents by using a single scene-centric encoder to generate a unified representation of the entire scene context. This unified encoding is then shared across all agent predictions, combining the computational benefits of a single processing pass with the accuracy benefits of agent-specific predictions through the decoding stage.
2Measurement precision
If agent-centric neural networks process each agent separately, then individual agent predictions are accurate, but overall computational efficiency decreases
Solution Approach 1:
The scene-centric encoder serves as a universal component that generates a single scene representation used for predicting trajectories of multiple different agents. This multi-functional encoding approach allows the same computational process to serve multiple purposes (predicting for agent 1, agent 2, agent 3, etc.), significantly improving computational efficiency compared to having separate encoders for each agent while maintaining prediction accuracy through agent-specific decoders.
3Productivity
If scene-centric encoding is used for all agents, then computational efficiency is improved, but individual agent context may be lost
Solution Approach 1:
The agent-centric decoder acts as an intermediary component that bridges the gap between the shared scene-centric encoding and individual agent predictions. It takes the general scene representation and incorporates agent-specific features and context, transforming the unified encoding into personalized predictions for each agent. This intermediary stage ensures that no agent-specific context is lost despite the shared encoding approach.
Data Source
AI summary
A method performed by one or more computers, the method comprising: obtaining scene context data characterizing a scene in an environment at a current time point, wherein the scene context data includes features of the scene in a scene-centric coordinate system; generating a scene-centric encoded representation of the scene in the environment by processing the scene context data using an encoder neural network; for each target agent: obtaining agent-specific features for the target agent, processing the agent-specific features for the target agent and the scene-centric encoded representation of the scene using a fusion neural network to generate a fused scene representation for the target agent, and processing the fused scene representation for the target agent using a decoder neural network to generate a trajectory prediction output for the target agent in an agent-centric coordinate system for the target agent.