Autonomous Driving Algorithm Scoring From Telematics Performance Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity and diversity of autonomous driving algorithms make it difficult to compare and determine their safety and efficacy, especially in varying conditions, leading to challenges in ascertaining when one algorithm is safer than another or when a combination of algorithms is more unsafe.
Innovation Solution
A computing device analyzes telematics data from vehicles to determine performance metrics of autonomous driving algorithms, comparing them to metrics from other algorithms, human drivers, or ideal performance standards, assigning an autonomous vehicle score that can trigger the replacement of underperforming algorithms with safer ones and influence insurance premiums.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous driving algorithms are made more complex to handle all aspects of driving tasks, then the degree of automation and functionality are improved, but the difficulty of comparing and evaluating their safety and efficacy increases
Solution Approach 1:
The patent introduces an intermediary evaluation system that uses telematics data and standardized metrics to objectively assess autonomous driving algorithm performance. This intermediary layer mediates between the complex algorithms and the need for simple safety comparison, providing a standardized framework that translates complex algorithm behaviors into comparable safety and efficacy scores.
Solution Approach 2:
The patent replaces subjective human evaluation of autonomous driving performance with an automated computational evaluation system. Instead of relying on human judgment to assess algorithm safety, the system uses telematics data analysis and automated metrics to objectively measure and compare algorithm performance, substituting mechanical/computational processes for human cognitive evaluation.
2Reliability
If multiple autonomous driving algorithms are executed in the same vehicle to improve safety, then the reliability is improved, but the difficulty of determining when combinations are unsafe increases
Solution Approach 1:
The patent segments the evaluation of multiple algorithms into distinct, measurable metrics that can be independently assessed and then combined. Instead of evaluating the complex interaction of multiple algorithms as a whole, the system breaks down performance into separable components such as lane-keeping accuracy, collision avoidance, and response time, making it easier to detect and measure safety characteristics of algorithm combinations.
Solution Approach 2:
The patent implements a feedback mechanism where telematics data continuously monitors algorithm performance in real-world conditions and feeds this information back into the evaluation system. This feedback loop enables the system to detect unsafe algorithm combinations by analyzing actual performance outcomes and adjusting evaluations based on observed safety incidents or near-misses.
3Measurement precision
If telematics data is collected and analyzed to evaluate algorithm performance, then the measurement precision is improved, but the loss of time and computational resources increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining evaluation metrics and thresholds before actual algorithm performance assessment. The system establishes standardized measurement criteria and safety thresholds in advance, so that when telematics data is collected, the evaluation process can quickly match observed performance against pre-established benchmarks, reducing the time required for analysis while maintaining measurement precision.
Data Source
AI summary
Methods and systems for autonomous driving algorithm evaluation are described herein. A computing device may receive, via telematics sensors associated with a vehicle, telematics data corresponding to one or more trips taken by the vehicle during a period of time. Portions of the telematics data corresponding to use of an autonomous driving algorithm may be determined. One or more performance metrics of the autonomous driving algorithm may be determined based on the portions of the telematics data corresponding to use of the autonomous driving algorithm. The one or more performance metrics may be compared to one or more other performance metrics, such as those corresponding to other autonomous driving algorithms. An autonomous vehicle score may be assigned to the autonomous driving algorithm. Based on the autonomous vehicle score, an indication of a second autonomous driving algorithm may be sent to the vehicle.