Driver Assistance Tuning Using Fleet Telemetry Profiles
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Solution Overview
Problem
Existing driver assistance systems fail to account for the unique driving styles and preferences of individual drivers, leading to suboptimal performance and user dissatisfaction.
Innovation Solution
A data-driven approach that collects telemetry data from a fleet of vehicles, clusters driver profiles, and uses machine learning to generate personalized tuning parameters for driver assistance systems, tailoring settings to individual driving habits and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If driver assistance systems use standardized settings for all users, then device complexity is reduced and ease of manufacture is improved, but adaptability to individual driving styles deteriorates and user satisfaction decreases
Solution Approach 1:
The system performs preliminary clustering of driver profiles using telemetry data from fleet vehicles before the individual driver uses the system. This pre-processing creates ready-to-use driver profiles that can be quickly matched to new drivers, avoiding the need for complex real-time analysis when the driver first uses the system.
Solution Approach 2:
The system creates simplified copies of complex driver behaviors by clustering telemetry data into representative driver profiles. Instead of analyzing each driver's complete telemetry history in real-time, the system uses pre-generated profile copies that capture essential driving style characteristics, reducing computational complexity while maintaining adaptability.
2Adaptability or versatility
If telemetry data from fleet vehicles is collected and processed, then adaptability to individual drivers is improved, but loss of time for data processing and system setup increases
Solution Approach 1:
Driver profile clustering is performed in advance using telemetry data collected from fleet vehicles, creating ready-to-use profiles before individual drivers need them. This preliminary processing eliminates the need for time-consuming real-time data analysis when drivers first use the system, reducing setup time while maintaining personalization capability.
Solution Approach 2:
The system dynamically matches individual drivers to pre-clustered profiles based on their telemetry data characteristics. This dynamic matching process is computationally efficient compared to full profile generation, allowing quick personalization without extensive processing time while still adapting to individual driving styles.
3Measurement precision
If machine learning algorithms are trained with baseline datasets, then measurement precision of driver behavior patterns is improved, but productivity of system deployment is reduced
Solution Approach 1:
Machine learning algorithms are trained in advance using baseline datasets derived from clustered telemetry data, creating pre-trained models before system deployment. This preliminary training achieves high measurement precision for driver behavior patterns while avoiding the need for time-consuming real-time training during system deployment, thus maintaining both accuracy and deployment speed.
Data Source
AI summary
A computer-implemented method comprises: receiving first telemetry data generated by sensors of respective first vehicles in a fleet; clustering the first telemetry data into groups, each of the groups representing a profile of one or more first drivers of the first vehicles in the fleet; receiving second telemetry data generated by sensors of a second vehicle controlled by a second driver; associating the second driver with a first group of the groups by classifying the received second telemetry data; providing a subset of the first telemetry data corresponding to the first cluster as a baseline dataset for training of machine learning algorithms; generating baseline tuning parameter values using the trained machine learning algorithms; and providing the baseline tuning parameter values to a driver assistance system of a third vehicle controlled by the second driver.