Vehicle Trajectory Uncertainty Estimation for Out-of-Distribution Scenarios
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle trajectory prediction methods lack effective uncertainty estimation, particularly for out-of-distribution scenarios, and fail to analyze the importance of different features, requiring specialized predictors.
Innovation Solution
A generic direct-error-prediction-based uncertainty estimation framework that ranks static data points by predicted error scores, selects relevant locations for additional data collection, and trains a neural network decoder without detailed predictor design, using a data selection algorithm to evaluate feature significance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probabilistic inference approaches such as graphical models and dynamic Bayesian networks are used for vehicle trajectory prediction, then the prediction framework can provide analysis of feature importance, but the approach requires detailed predictor design and cannot effectively handle out-of-distribution scenarios
Solution Approach 1:
The patent applies universality by creating a generic uncertainty estimation framework that works with any trajectory predictor (e.g., LSTMs, GRUs, Transformers) without requiring predictor-specific design. The framework uses a universal error prediction model that can estimate uncertainty for out-of-distribution scenarios across different predictor architectures, eliminating the need for detailed predictor-specific probabilistic inference design.
Solution Approach 2:
The patent introduces an intermediary error prediction model that mediates between the trajectory predictor and uncertainty estimation. This intermediary component predicts errors of the main predictor and uses these predictions to estimate uncertainty, providing a bridge that enables general uncertainty estimation without modifying the original predictor design.
2Adaptability or versatility
If specialized predictors are designed to provide uncertainty analysis, then feature importance can be analyzed, but the approach fails for out-of-distribution scenarios and requires detailed predictor design
Solution Approach 1:
The framework achieves universality by designing a generic uncertainty estimation module that can be applied to any trajectory predictor without customization. The error prediction model and data selection algorithm work universally across different predictor types, enabling the system to handle both in-distribution and out-of-distribution scenarios reliably.
Solution Approach 2:
The patent applies preliminary action by collecting additional data from identified locations before final prediction. The system proactively identifies locations with high uncertainty using the data selection algorithm, collects additional data from these locations, and uses this pre-collected data to improve uncertainty estimation reliability for out-of-distribution scenarios.
3Productivity
If data collection is performed without targeted selection, then comprehensive data is available for training, but the efficiency of data collection and training is reduced
Solution Approach 1:
The patent applies local quality by focusing data collection efforts on specific locations with high uncertainty rather than uniformly collecting data everywhere. The data selection algorithm identifies specific locations where additional data would be most valuable, and the system collects data selectively from these locations, improving both efficiency and accuracy.
Solution Approach 2:
The system uses feedback from the error prediction model to guide data collection. The error prediction model continuously evaluates uncertainty at different locations, and this feedback drives the data selection algorithm to identify and prioritize locations where additional data collection would most improve uncertainty estimation accuracy.
Data Source
AI summary
Systems and methods for uncertainty estimation in vehicle trajectory prediction are provided. In one embodiment, a method includes calculating predicted error scores for each static data point of a set of static data points for a new traffic scenario. A static data point corresponds to a location of the new traffic scenario. The method includes ranking the static data points of the set of static data points based on the predicted error scores. The method further includes selecting a predetermined percentage of the ranked static data points having the highest predicted error scores of the ranked static data points. The method includes identifying locations of the new traffic scenario corresponding to the selected ranked static data points. The method includes collecting additional data points from the identified locations of the new traffic scenario. The method includes training a decoder for a neural network based on the additional data points.