Cloud-Based Driving Score Generation With IMU Sensor Fusion
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Solution Overview
Problem
Conventional driver score rating systems lack the integration of inertial measurement unit (IMU) sensors and data fusion from driver behavior inputs from cameras and wearables, leading to inaccurate and non-scalable scoring calculations, which are detrimental to predicting driver behavior and driving style.
Innovation Solution
A cloud-based driving score report generating system using artificial intelligence and machine learning to process data from multiple sensing sources, including IMU sensors, cameras, and wearables, to generate accurate driving style and eco-friendliness score ratings, and enable data sharing with service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional algorithms are used for scoring calculations, then the system is simple to implement, but the system cannot scale up to increasing amounts of data inputs and sensors
Solution Approach 1:
The patent replaces conventional algorithms with artificial intelligence and machine learning processing systems. This substitution enables the system to handle increasing amounts of data from multiple sensors (cameras, wearables, IMU, vehicle sensors) while maintaining accurate driver behavior prediction. The AI/ML infrastructure provides scalable processing capabilities that conventional algorithms cannot achieve, allowing the system to accommodate additional sensing sources without fundamental redesign.
2Measurement precision
If data from multiple sensing sources (IMU, cameras, wearables) is integrated, then the accuracy of predicting driver behavior is improved, but the complexity of data processing increases
Solution Approach 1:
The patent merges data from multiple sensing sources including IMU sensors, cameras, wearables, and vehicle sensors into a unified processing framework. This consolidation allows the system to leverage complementary information from each source to improve driver behavior prediction accuracy. The AI/ML processing system integrates these diverse data streams, fusing them to create a comprehensive view of driver state and behavior that exceeds the capability of any single sensor type.
Solution Approach 2:
The patent introduces cloud-based AI/ML processing infrastructure as an intermediary between the distributed sensing devices and the scoring system. This intermediary layer handles the complex tasks of data aggregation, preprocessing, fusion, and analysis, thereby simplifying the overall system architecture. The cloud-based processing mediates the integration of heterogeneous data sources, managing the complexity of multi-sensor fusion while delivering accurate predictions to the scoring system.
3Measurement precision
If inertial measurement unit (IMU) sensors and driver behavior inputs are processed, then the accuracy of driver scoring is improved, but computational resources and time are consumed
Solution Approach 1:
The patent implements preliminary processing of IMU and driver behavior data through edge computing capabilities on the vehicle's processor before transmitting to the cloud. This preliminary action includes data filtering, feature extraction, and preliminary analysis that reduces the volume and complexity of data requiring cloud-based AI/ML processing. By performing initial processing locally, the system reduces computational load and transmission requirements while maintaining prediction accuracy.
Solution Approach 2:
The patent employs continuous data streaming and incremental learning approaches where the AI/ML system processes data in continuous flows rather than batch processing. This allows the system to maintain accurate predictions in real-time while efficiently utilizing computational resources. The continuous processing enables the system to adapt to changing driving conditions dynamically without requiring complete reprocessing of historical data, thereby reducing overall computational time and resource consumption.
Data Source
AI summary
Disclosure relates to a driving score generating system. The system comprises a driving environment, one or more sensing devices within the driving environment and a processor comprising an artificial neural network. The processor may be operable to generate and execute a plurality of instructions stored thereon. The processor may be further operable to receive sensing information from the one or more sensing device and generate a driving score report in response to the sensing information received from the one or more sensing devices. The driving score report comprises a driving style score rating and an eco-friendliness score rating. A cloud-based data sharing system and a method of generating a driving score report is also disclosed.