Agent-Based Navigation Simulation for Rare Event Likelihood
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Solution Overview
Problem
Current methods for simulating navigation interactions in autonomous vehicles lack the ability to accurately determine the likelihood of rare events, such as collisions, which are crucial for safety-critical deployments, as they require large quantities of real-world data and are time-consuming to collect.
Innovation Solution
A system that generates multiple simulations of navigation interactions by sampling from agent data probability distributions, allowing for the identification of agents and their properties, and determining the likelihood of specific events by analyzing these simulations, thereby providing a more realistic and efficient assessment of potential risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large quantities of real-world data are collected to determine the likelihood of rare events, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent creates virtual copies of real-world navigation interactions through computer simulations. Instead of collecting large quantities of real-world data, the system generates synthetic interaction data by simulating navigation scenarios with virtual agents, thereby obtaining sufficient data for likelihood determination without time-consuming real-world collection
Solution Approach 2:
The patent performs preliminary simulations to generate probability distributions for agent behaviors before actual likelihood determination is needed. By pre-generating these distributions from simulated data, the system avoids the need to collect large quantities of real-world data at the time of likelihood assessment
2Reliability
If probability distributions are generated from logged agent data, then reliability of behavior predictions is improved, but device complexity increases
Solution Approach 1:
The patent introduces probability distributions as an intermediary layer between logged agent data and behavior predictions. Instead of directly using raw logged data, the system processes this data into probability distributions that capture behavioral patterns, thereby improving prediction reliability while managing complexity through structured intermediate representations
3Productivity
If multiple simulations are generated by sampling from probability distributions, then productivity in determining event likelihood is improved, but manufacturing precision of simulation results decreases
Solution Approach 1:
The patent generates multiple simulations by sampling from probability distributions, performing more simulations than a single real-world observation would provide. This excessive action approach ensures sufficient statistical samples for accurate likelihood determination while maintaining computational efficiency through the use of pre-established probability distributions
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining the likelihood that a particular event would occur during a navigation interaction using simulations generated by sampling from agent data. In one aspect, a method comprises: identifying an instance of a navigation interaction that includes an autonomous vehicle and agents navigating in an environment; generating multiple simulated interactions corresponding to the instance, comprising, for each simulated interaction: identifying one or more agents; for each identified agent and for each property that characterizes behavior of the identified agent, obtaining a probability distribution for the property; sampling a respective value from each of the probability distributions; and simulating the navigation interaction in accordance with the sampled values; and determining a likelihood that the particular event would occur during the navigation interaction based on whether the particular event occurred during each of the simulated interactions.