Cloud AI Service Architecture for Automated Driving Control
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Solution Overview
Problem
Existing CAVH systems lack comprehensive connectivity, sensing, control, planning, maintenance, security, and privacy protection functionalities, necessitating a unified cloud-based system to enhance their efficiency and safety.
Innovation Solution
A cloud-based CAVH service system comprising sub-systems like Planning as a Service, Sensing as a Service, Control as a Service, Prediction/Analysis as a Service, Security as a Service, Privacy as a Service, and Connectivity as a Service, which interact with user, vehicle, and infrastructure components to provide integrated services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a cloud-based system is implemented to provide comprehensive CAVH services, then system functionality and service integration are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The cloud-based CAVH system is divided into multiple independent service modules including Planning as a Service, Sensing as a Service, Control as a Service, Prediction/Analysis as a Service, Security as a Service, Privacy as a Service, and Connectivity as a Service. Each module operates independently and can be deployed separately, allowing the system to provide comprehensive functionality while maintaining manageable complexity through modular architecture.
2Productivity
If multiple service sub-systems are integrated in the cloud-based CAVH system, then service completeness and operational efficiency are improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The cloud-based platform provides a universal service framework where a single system delivers multiple CAVH services through standardized interfaces. The platform can simultaneously support planning, sensing, control, prediction, security, privacy, and connectivity functions, allowing operational efficiency to improve while maintaining ease of operation through a unified access point and consistent service delivery mechanism.
3Reliability
If cloud-based services are deployed to enhance CAVH system capabilities, then system performance and safety are improved, but infrastructure requirements and implementation cost increase
Solution Approach 1:
The cloud-based CAVH system incorporates self-service capabilities where the platform automatically manages service deployment, resource allocation, and system configuration. The system can autonomously provision services, monitor performance, and adjust resources based on demand, reducing the need for manual intervention and lowering implementation and operational costs while maintaining high safety and reliability standards through automated quality assurance mechanisms.
Data Source
AI summary
The invention presents a cloud-based model deployment and control system (CMDCS) for providing automated driving services. The CMDCS comprises a cloud-based platform, an onboard unit (OBU), and a Vehicle-to-System component. The cloud-based platform comprises a localization-enhancement subsystem and a cloud computing module. The CMDCS is configured to collect detectable data and undetectable data from vehicles, road, and cloud. Then, the CMDCS deploys a set of end-to-end AI models and methods for automated driving services, comprising sensing, prediction, planning, and control services. The AI models and methods are trained and optimized to process collected data for providing operating parameters for vehicles. Then, the vehicles can be effectively and efficiently controlled and operated by the CMDCS. In addition, the CMDCS is configured to generate and provide detailed time-sensitive vehicle specific control instructions.