Pedestrian Prediction Device Using Latent Variable Uncertainty Modeling
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Solution Overview
Problem
Existing systems for predicting pedestrian movement in machine control and driving assistance fail to accurately account for uncertainty in sensor data and agent behavior, leading to limited hypothesis spaces and reduced accuracy.
Innovation Solution
A prediction device that models pedestrians as states with multiple latent variables, using a fully probabilistic interaction model to quantify and propagate uncertainty, incorporating position, velocity, orientation, and semantic information from sensors, and allowing for resource-efficient modeling of different traffic agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems model pedestrian movement without uncertainty, then computational resources are saved and model simplicity is maintained, but prediction accuracy and reliability deteriorate
Solution Approach 1:
The patent transforms deterministic movement parameters into probabilistic parameters by introducing uncertainty distributions. Instead of modeling pedestrian position and velocity as fixed values, the system models them as probability distributions that evolve over time, allowing the model to capture the inherent uncertainty in pedestrian behavior while maintaining computational tractability through efficient sampling and propagation methods
Solution Approach 2:
The patent segments the pedestrian state into multiple latent variables (position, velocity, acceleration, orientation, etc.) each with its own uncertainty distribution. This segmentation allows the complex uncertainty modeling to be broken down into manageable components that can be propagated independently and then combined, reducing overall computational complexity while improving prediction accuracy
2Reliability
If existing systems use restricted hypothesis space, then computational efficiency is improved, but prediction reliability and accuracy deteriorate
Solution Approach 1:
The patent implements dynamic hypothesis space that adapts to the specific situation. The system starts with a broad hypothesis space that includes all possible pedestrian behaviors, then dynamically prunes unlikely hypotheses based on observed sensor data and contextual information. This allows the system to maintain high reliability by considering multiple possibilities while improving computational efficiency by focusing computational resources on the most likely scenarios
Solution Approach 2:
The patent changes the parameter representation from deterministic values to probability distributions, allowing the system to quantify uncertainty explicitly. By using probabilistic parameters, the system can maintain a rich hypothesis space that captures multiple possible futures while using efficient probabilistic inference algorithms to compute predictions, thereby balancing reliability and computational efficiency
3Measurement precision
If probabilistic information is stored for all agents, then prediction accuracy improves, but resource consumption increases
Solution Approach 1:
The patent applies local quality by storing probabilistic information selectively based on the agent type and situation. High-priority agents such as pedestrians in the vehicle's path receive full probabilistic modeling with multiple latent variables, while low-priority agents use simplified deterministic models. This localized application of probabilistic modeling optimizes resource usage while maintaining tracking precision where it matters most
Data Source
AI summary
A prediction device is described for predicting a location of a pedestrian moving in an environment. The prediction device may have a memory configured to store a probability distribution for multiple latent variables indicating one or more states of the one or more pedestrians. The prediction device may be configured to predict a position of a pedestrian for which no position information is currently available from the probability distribution of the multiple latent variables.