Cloud Vehicle Performance Optimization via Crowdsourced Sensor Data
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Solution Overview
Problem
Conventional vehicle technologies fail to optimize vehicle performance effectively across various parameters such as driving experience, fuel consumption, and longevity due to limitations in tracking and responding to intrinsic and extrinsic factors like tire wear, engine conditions, traffic, weather, and driving behavior.
Innovation Solution
A cloud-based system that utilizes sensor data from vehicles, crowdsourced data, and weather information to generate recommendations for optimizing vehicle performance by associating vehicle characteristics with operational adjustments, such as tire changes or route modifications, through a model that consolidates data from similar vehicles and human feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional vehicle technologies are used, then vehicle operation is simple, but vehicle performance optimization is insufficient
Solution Approach 1:
A cloud-based system acts as an intermediary between multiple vehicles and users. The system receives sensor data from vehicles, processes it through consolidation models, and generates optimized recommendations. This intermediary approach allows complex data processing and optimization algorithms to run remotely in the cloud, rather than requiring complex hardware in each vehicle, thus improving performance optimization while keeping individual vehicle systems relatively simple.
Solution Approach 2:
The system enables vehicles to automatically share their sensor data and operational characteristics with the cloud platform. Each vehicle self-subscribes to data collection, and the system automatically processes this data through consolidation models to generate recommendations. This self-service mechanism reduces the need for manual intervention and complex control systems in individual vehicles while achieving comprehensive performance optimization across the fleet.
2Measurement precision
If data from multiple vehicles is consolidated, then optimization accuracy improves, but data processing complexity increases
Solution Approach 1:
The data processing is segmented into distinct functional modules: data reception from multiple vehicles, data consolidation through characteristic models, recommendation generation, and feedback delivery. By dividing the complex processing task into separate stages handled by different system components, the patent manages to improve optimization accuracy through comprehensive data analysis while keeping the complexity distributed and manageable across the system architecture.
Solution Approach 2:
The cloud-based system serves as an intermediary that handles the complex task of consolidating and processing data from multiple vehicles. Rather than requiring each vehicle to process complex multi-vehicle data, the cloud platform centralizes this processing function, using consolidation models to synthesize data from various sources and generate optimized recommendations, thereby improving accuracy while managing processing complexity centrally.
3Reliability
If real-time sensor data is collected, then performance monitoring improves, but energy consumption increases
Solution Approach 1:
The patent extracts the computationally intensive data processing and analysis functions from the vehicles and relocates them to the cloud-based system. Vehicles only need to collect sensor data and transmit it periodically, rather than continuously processing and analyzing it locally. This extraction of complex processing functions reduces the energy consumption requirements for onboard computing hardware while maintaining real-time performance monitoring capabilities through centralized analysis.
Solution Approach 2:
The system implements periodic data collection and transmission rather than continuous real-time processing. Vehicles transmit sensor data at scheduled intervals to the cloud platform, which then performs batch processing and analysis. This periodic approach reduces the energy consumption associated with continuous data transmission and local processing, while still providing timely performance monitoring and optimization recommendations.
Data Source
AI summary
Tracking intrinsic and extrinsic vehicle parameters for optimizing vehicle performance is presented herein. A method can include consolidating sets of data associated with a vehicle to generate a model; generating, using the model, recommendation data representing a recommendation associated with an operation of the vehicle; and in response to receiving a request from a device of the vehicle corresponding to the operation of the vehicle, sending, based on the request, a message comprising the recommendation directed to the device of the vehicle. In an example, the method can further include determining, based on crowdsourced data representing the characteristic of the element of the vehicle, whether the characteristic satisfies a defined condition with respect to the operation of the vehicle.