Autonomous Vehicle Simulation Collision Filtering for False Positive Triage
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Solution Overview
Problem
Simulations of autonomous vehicles often generate unrealistic collisions due to unreactive agents and scenarios, leading to inefficient collision triage analysis, which is time-consuming and resource-intensive.
Innovation Solution
A simulation system that classifies simulated collisions as realistic or unrealistic by considering the nature of the agent, collision avoidability, and similarity to known false positives, filtering out unrealistic collisions from in-depth analysis to focus on actionable scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all simulated collisions are analyzed in detail through collision triage, then comprehensive validation of autonomous vehicle behaviors is achieved, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent segments the collision analysis process into two distinct phases: a quick classification phase that filters collisions into realistic vs. unrealistic categories, and a detailed triage phase that analyzes only the realistic collisions. This segmentation allows the system to maintain comprehensive validation for important cases while avoiding waste of time on obviously unrealistic scenarios.
Solution Approach 2:
The patent implements a preliminary classification step before the detailed collision triage process. By pre-filtering collisions based on basic criteria (such as collision type, agent behavior patterns, and scenario plausibility), the system prepares the data in advance to identify which collisions warrant detailed analysis, thereby reducing overall processing time while maintaining validation quality.
2Measurement precision
If detailed collision triage analysis is performed on all simulated collisions, then root causes can be identified, but computational resources are wasted on unrealistic scenarios
Solution Approach 1:
The patent divides the collision analysis workload into two segments: a lightweight classification stage that quickly identifies unrealistic collisions, and a resource-intensive detailed triage stage that focuses only on realistic collisions. This ensures that computational resources for root cause identification are concentrated on cases where such analysis will be meaningful, thereby maintaining measurement precision while improving overall productivity.
Solution Approach 2:
The patent extracts and removes unrealistic collisions from the set of collisions requiring detailed triage analysis. By identifying and excluding these unrealistic scenarios through preliminary classification criteria, the system prevents waste of computational resources on scenarios that cannot provide meaningful root cause information, thus improving analysis efficiency without compromising the accuracy of root cause identification for valid cases.
3Reliability
If comprehensive collision analysis is performed without filtering, then no realistic collisions are missed, but the majority of time is spent on unrealistic false positives
Solution Approach 1:
The patent applies preliminary classification criteria to filter out unrealistic collisions before they enter the detailed triage pipeline. This preliminary action uses readily available simulation data (such as agent reaction patterns, collision geometry, and scenario context) to quickly identify and exclude false positives, ensuring that comprehensive analysis is applied only to plausible cases and thereby maintaining detection completeness while improving throughput.
Solution Approach 2:
The patent segments the collision processing workflow into a filtering stage and an analysis stage. The filtering stage uses simple, fast criteria to separate realistic from unrealistic collisions, while the analysis stage performs comprehensive triage only on the realistic subset. This segmentation ensures that no realistic collisions are missed (maintaining reliability) while avoiding the productivity penalty of analyzing unrealistic false positives.
Data Source
AI summary
Collision filtering for autonomous vehicle simulations is disclosed herein. A simulation system can be configured to identify a simulated collision involving a simulated autonomous vehicle and a simulated agent representing an obstacle with which the simulated autonomous vehicle may collide. For example, the simulated collision can be simulated based on logged data from a real-world autonomous vehicle. The simulation system can classify the simulated collision as unrealistic or realistic. For example, classification can involve considering a nature of the simulated agent (including, e.g., whether the simulated agent is a drivable obstacle), a nature of the collision, whether the collision is avoidable through an evasive maneuver, whether the collision is similar to a known false positive, and/or input from a human operator. The simulation system can filter the simulated collision from participating in a collision triage analysis in response to classifying the simulated collision as an unrealistic simulated collision.