Autonomous Vehicle Task Assignment Under Imperfect Sensing
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Solution Overview
Problem
Current methods for task scheduling in autonomous vehicles assume perfect sensing conditions, which is not realistic due to manufacturing defects or aging of sensors, leading to ineffective optimized assignment and scheduling during actual execution.
Innovation Solution
A processor-implemented method and system for optimized assignment of traversal tasks under imperfect sensing, using a statistical error model to estimate completion time and collision count, and assigning tasks based on these metrics to generate an optimized traversal task plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional task scheduling algorithms are used that assume perfect sensing conditions, then the system complexity remains low, but the task execution reliability deteriorates due to sensor errors from manufacturing defects or aging
Solution Approach 1:
The patent changes the parameter of sensing conditions from ideal to imperfect by incorporating statistical error models that account for sensor manufacturing defects and aging. This allows the task scheduling algorithm to adapt to real-world sensor imperfections, improving task execution reliability while maintaining manageable system complexity through mathematical modeling rather than complex hardware redundancy
Solution Approach 2:
The patent introduces an intermediary statistical error model that mediates between the imperfect sensor data and the task scheduling decision-making process. This model characterizes sensor errors and allows the scheduling algorithm to compensate for sensing inaccuracies, thereby improving reliability without requiring complex sensor replacement or calibration systems
2Productivity
If task assignment is optimized based on perfect sensing assumptions, then the productivity increases, but the measurement precision deteriorates because sensor errors are not accounted for in performance metrics
Solution Approach 1:
The patent modifies the performance metric calculation by incorporating sensor error statistics into the measurement model. This allows productivity optimization while maintaining measurement precision, as the algorithm now accounts for sensing uncertainties when evaluating task completion time and collision probability
Solution Approach 2:
The patent implements feedback by using statistical error models to continuously adjust task assignment decisions based on expected sensor performance. This feedback mechanism ensures that productivity optimization does not compromise measurement precision, as the system adapts to actual sensor capabilities rather than assuming perfect measurements
3Adaptability or versatility
If sensor error models are incorporated into task scheduling, then the adaptability to imperfect sensing conditions improves, but the computational complexity increases due to additional calculations required
Solution Approach 1:
The patent transforms the complex problem of adapting to imperfect sensing by changing the approach from handling individual sensor errors to using statistical error models. This parameter transformation allows the system to adapt to sensing conditions through manageable mathematical calculations rather than complex real-time sensor validation
Solution Approach 2:
The patent applies preliminary action by pre-characterizing sensor errors through statistical error models before task execution. This preliminary characterization of sensor imperfections allows the task scheduling algorithm to compensate for errors in advance, reducing the need for complex real-time computational adjustments during actual task execution
Data Source
AI summary
The disclosure relates generally to methods and systems for optimized assignment of traversal tasks under imperfect sensing of autonomous vehicles. Most of the techniques assumes perfect equipment conditions. With the imperfect sensing, most of the optimized assignment and scheduling algorithm may not be effective during actual execution of the tasks. The present disclosure solves the technical problems in the art by providing an analytical model which estimates the basic performance metrics such as an expected travel duration and safety estimation such as collision probability on its path, under imperfect sensing, for optimal assignment of the tasks. An analytical model is integrated with a performance estimator as implemented by the systems of the present disclosure, which tracks, predicts, and alerts on any major deviations from its intended performance of safety parameters.