Anchor Trajectory Prediction for Uncertainty-Aware Vehicle Motion
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Solution Overview
Problem
Conventional trajectory prediction systems for autonomous vehicles are inefficient in generating accurate future trajectory predictions, often requiring excessive computational resources and experiencing mode collapse by predicting a single most-likely trajectory without capturing inherent uncertainty.
Innovation Solution
A system that uses a trajectory prediction neural network to generate a compact trajectory prediction output by processing embeddings of agents in the environment, characterizing the predicted similarity of future trajectories to anchor trajectories, which represent possible intentions like U-turns or lane changes, and includes uncertainty analysis to avoid mode collapse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trajectory prediction systems generate multiple possible future trajectories to capture uncertainty, then prediction accuracy is improved, but computational resources and processing time are excessively consumed
Solution Approach 1:
The system segments the continuous space of possible trajectories into a discrete set of K anchor trajectories. Each anchor trajectory represents a distinct motion pattern (e.g., continue straight, turn left, turn right). By segmenting the prediction space, the system captures multiple possible future behaviors without requiring exhaustive computation of all possible trajectories, thus resolving the contradiction between prediction accuracy and computational efficiency.
Solution Approach 2:
The system changes the parameter representation from generating multiple complete trajectory sequences to generating a compact set of parameters: similarity scores indicating the likelihood of each anchor trajectory. This parameter transformation reduces computational complexity while preserving the ability to represent uncertainty across multiple possible futures, addressing the efficiency-accuracy tradeoff.
2Productivity
If conventional systems predict a single most-likely trajectory to reduce computational complexity, then processing speed is improved, but mode collapse occurs and inherent uncertainty is not captured
Solution Approach 1:
The system dynamically adapts the prediction output to match the complexity of the situation. When uncertainty is high, the similarity scores across multiple anchor trajectories will be more distributed, allowing the system to represent multiple plausible futures. When uncertainty is low, the scores concentrate on the most likely trajectory. This dynamic representation maintains processing speed while reliably capturing uncertainty when present.
Solution Approach 2:
The system uses the computed similarity scores as feedback to the planning system, enabling the planner to account for prediction uncertainty. The feedback mechanism allows the planner to prepare for multiple possible outcomes rather than relying on a single predicted trajectory, thus maintaining reliability without sacrificing processing speed.
3Measurement precision
If detailed trajectory predictions are generated to improve accuracy, then prediction quality is improved, but the representation becomes complex and resource-intensive
Solution Approach 1:
The system extracts only the essential information needed for trajectory prediction by computing similarity scores between agent embeddings and anchor trajectories. Instead of generating complete detailed trajectory sequences, the system extracts compact similarity representations that capture the essential predictive information with minimal complexity, resolving the contradiction between prediction quality and representation complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using anchor trajectories.