Mobile Roadside Edge Computing for Dynamic Autonomous Driving Support
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Solution Overview
Problem
Current intelligent roadside systems are primarily fixed and immobile, limiting their ability to adapt and enhance automated driving functions for connected and automated vehicle highway systems, especially in dynamic and complex scenarios.
Innovation Solution
The development of a Mobile Intelligent Roadside Infrastructure System (MIRIS) that includes a Mobile Roadside Intelligent Unit (MRIU), Traffic Operation Center (TOC), Traffic Control Center (TCC), and roadside communication system, supported by a multi-level cloud platform, high-precision map system, and information security system, enabling dynamic deployment and enhanced automated driving functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If fixed and immobile infrastructure components are used, then system stability is improved, but adaptability and flexibility deteriorate
Solution Approach 1:
The patent introduces mobile intelligent roadside units that can dynamically relocate along the roadway, transforming the static infrastructure into a dynamic system. These mobile units can be deployed to different locations based on real-time traffic conditions, accident scenarios, or construction zones, providing both stability through standardized unit design and adaptability through flexible positioning.
2Ease of manufacture
If fixed infrastructure components are used, then deployment simplicity is improved, but service capability in diverse scenarios deteriorates
Solution Approach 1:
The mobile intelligent roadside units are designed as universal, multi-functional components that can serve multiple purposes across different scenarios. Each unit contains standardized sensing, computing, and communication capabilities that can be deployed in various configurations to handle common driving scenarios, emergency situations, and long-tail events, replacing the need for multiple specialized fixed infrastructure components.
3Adaptability or versatility
If mobile intelligent roadside units are deployed, then adaptability and flexibility are improved, but system complexity increases
Solution Approach 1:
The system is segmented into standardized mobile intelligent roadside units that can operate independently or in coordination. Each unit is a self-contained module with integrated sensing, computing, and communication functions, allowing the system to scale flexibly without proportionally increasing overall complexity. The modular design enables simple deployment scenarios where units can be added or relocated without reconfiguring the entire system.
4Reliability
If mobile intelligent roadside units are used, then emergency scenario management is improved, but infrastructure cost increases
Solution Approach 1:
Mobile intelligent roadside units can be dynamically deployed to emergency scenes, providing enhanced sensing and monitoring capabilities only when and where needed. This dynamic deployment allows the system to maintain high reliability for emergency scenario management while optimizing infrastructure costs by avoiding permanent installation of specialized equipment at every potential emergency location.
Data Source
AI summary
Provided herein is an artificial intelligence-based mobile roadside intelligent unit (MRIU) for providing, supplementing, and/or enhancing the control and operation of autonomous vehicles in normal and long-tail scenarios. The MRIU comprises a computing module configured to provide supplemental computation capability for autonomous driving. The MRIU comprises a communication module to communicate and exchange data with a vehicle or a cloud. The MRIU provides prediction, decision-making, and/or control functions for autonomous driving. The MRIU provides edge computing capability for autonomous vehicles to train and operate artificial intelligence-based intelligent driving models in a distributed fashion. Specifically, an edge computing unit conducts data fusion and data feature extraction, provides prediction, formulates control strategies, generates vehicle control instructions, and/or distributes vehicle control information and/or instructions for an autonomous vehicle.