Cloud-Based Vehicle Torque Control for Driver-Adaptive AI
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Solution Overview
Problem
Existing vehicle power control systems are limited by low computational performance, unable to effectively implement AI algorithms due to hardware constraints, and struggle to optimize driving performance in response to external variables such as driver propensity, weather, and traffic conditions, compromising safety and efficiency.
Innovation Solution
A vehicle driving control system that utilizes a cloud server to collect and analyze driving data from multiple vehicles, enabling AI-based optimization of torque control by learning driver-specific parameters and external conditions, integrating with mobile devices and autonomous systems to enhance computational performance without hardware redesign.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI algorithms are implemented for optimal torque control, then driving performance and energy efficiency are improved, but hardware computational performance requirements increase beyond existing controller capabilities
Solution Approach 1:
The patent introduces a cloud server as an intermediary computational platform that performs AI-based torque optimization calculations. The server receives driving data from multiple vehicles, processes this data using AI algorithms to determine optimal torque control strategies, and transmits the results back to vehicle controllers. This mediator approach allows complex AI computations to be performed externally, enabling advanced driving performance optimization without requiring high-end hardware in the vehicle controllers themselves.
2Productivity
If computational performance of existing controller is increased, then AI algorithm processing capability is improved, but safety of legacy software code cannot be guaranteed
Solution Approach 1:
The patent segments the computational tasks between two distinct components: the existing vehicle controller that executes verified legacy safety-critical code, and a separate cloud server that handles non-critical AI-based optimization calculations. This segmentation allows the controller to maintain its original computational capabilities and software verification status, while the server performs the computationally intensive AI processing that would otherwise require upgrading the controller hardware and re-verifying safety.
3Adaptability or versatility
If cloud server is used to collect and analyze driving data, then personalized torque control is achieved, but data transmission and processing time increase
Solution Approach 1:
The patent merges data from multiple vehicles transmitted through the cloud server with locally available real-time driving data from the vehicle controller. By combining these data sources, the system achieves personalized torque control that reflects both collective learning from the fleet and immediate vehicle-specific conditions. This merging approach allows the system to leverage historical data for personalized optimization while minimizing latency by processing time-critical decisions locally with augmented insights from the cloud.
Data Source
AI summary
An embodiment server includes a processor configured to collect driving data based on a driver driving propensity or a driving situation from a plurality of vehicles and to change information for determining an output torque suitable for each of the vehicles by analyzing the driving data and a communication device configured to transmit the changed information for determining the output torque to the vehicles by communicating with the vehicles.