Autonomous Vehicle Localization Error Handling via Planner Comparison
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Solution Overview
Problem
Autonomous vehicles face safety concerns due to localization inconsistencies, which can cause them to disengage from autonomous mode and pull over, leading to safety issues and operational disruptions.
Innovation Solution
A system that utilizes a planner component to compare current and previous outputs, allowing the vehicle to continue driving autonomously even with localization inconsistencies by determining if the difference between outputs is within acceptable thresholds, and performing alternate operations such as handing over control or performing safety maneuvers when inconsistencies are significant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle disengages from autonomous mode and pulls over due to localization inconsistencies, then safety is improved, but operational continuity and productivity deteriorate
Solution Approach 1:
The planner component acts as an intermediary between the localization component and vehicle control. It receives localization outputs, compares current and previous outputs to detect inconsistencies, and decides whether to trust the localization data or switch to alternate operations. This mediator approach allows the system to maintain operational continuity while ensuring safety by filtering out harmful localization errors.
Solution Approach 2:
Instead of completely disengaging autonomous mode whenever localization inconsistencies occur, the system applies partial action by selectively trusting or disregarding localization outputs based on the severity and nature of the inconsistency. This allows the vehicle to continue autonomous operation in many cases while only switching to alternate operations when truly necessary, thus maintaining productivity while ensuring safety.
2Productivity
If the vehicle continues autonomous operation despite localization inconsistencies, then operational continuity is improved, but safety deteriorates
Solution Approach 1:
The system implements feedback by continuously comparing current planner outputs with previous outputs and monitoring localization consistency over time. This feedback mechanism allows the system to detect when localization errors become significant and switch to alternate operations, thereby maintaining safety while allowing continuous operation during normal conditions.
Solution Approach 2:
The system applies partial action by selectively continuing autonomous operation only when localization inconsistencies are within acceptable thresholds. When inconsistencies exceed thresholds, the system switches to alternate operations. This selective approach maximizes operational continuity while maintaining safety through conditional continuation rather than blanket continuation.
3Measurement precision
If the system implements strict localization verification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements partial verification by comparing planner outputs only when necessary - specifically when localization inconsistencies are detected or at scheduled intervals. Rather than continuously verifying every localization output with multiple complex checks, the system applies verification selectively, thus maintaining measurement precision while reducing unnecessary computational complexity.
Solution Approach 2:
The verification process is segmented into distinct stages: the localization component generates pose estimates, the planner component processes these estimates and compares current with previous outputs, and the system switches to alternate operations only when inconsistencies exceed thresholds. This segmentation allows each component to focus on its specific task, improving localization accuracy through specialized processing while managing overall system complexity through modular design.
Data Source
AI summary
Localization error handling using output is described. A computing system associated with a vehicle can receive sensor data from a sensor associated with vehicle. The computing system can determine, based at least partly on the sensor data, a first instruction for controlling the vehicle during a first period of time and a difference in pose information associated with a pose of the vehicle. Based at least partly on determining the difference, the computing system can retrieve a second instruction for controlling the vehicle during a second period of time prior to the first period of time and, based at least partly on comparing the first instruction and the second instruction, the computing system can determine whether the vehicle is to follow the first instruction or perform an alternate operation.