Autonomous Vehicle World Model Control via Roadside Infrastructure
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Solution Overview
Problem
Existing autonomous vehicles require expensive and complicated on-board systems, hindering widespread commercial implementation.
Innovation Solution
An Intelligent Road Infrastructure System (IRIS) that provides vehicles with customized, real-time control instructions through a network of roadside units, traffic control units, traffic control centers, vehicle onboard units, and cloud computing services, supporting sensing, prediction, planning, and decision-making for connected automated vehicle highway systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use expensive and complicated on-board systems, then vehicle autonomy and safety are improved, but system cost and complexity increase
Solution Approach 1:
The patent introduces roadside units (RSUs) and traffic control centers (TCCs) as intermediary systems between vehicles and the infrastructure. These intermediaries handle complex sensing, prediction, and decision-making tasks, allowing vehicles to use simpler onboard systems while maintaining high autonomy levels. The TCC acts as a central mediator that coordinates vehicle operations and manages traffic flow.
Solution Approach 2:
The patent shifts the computational burden from the vehicle dimension to the infrastructure dimension. By deploying sensors, processors, and communication networks along the road infrastructure (RSUs, TCCs, cloud services), the system moves autonomy capabilities from individual vehicles to the road network itself, reducing onboard complexity.
2Manufacturing precision
If autonomous vehicles use expensive and complicated on-board systems, then vehicle control accuracy is improved, but implementation cost increases
Solution Approach 1:
The patent creates a universal infrastructure system (IRIS) that serves multiple vehicles simultaneously. The same roadside units and traffic control centers provide sensing, communication, and control services to numerous vehicles, distributing the cost across many users. This multi-functional infrastructure reduces the per-vehicle implementation cost while maintaining high control accuracy.
Solution Approach 2:
The patent merges sensing, communication, control, and computation functions into an integrated infrastructure system. By combining these functions at the roadside and cloud level rather than requiring each vehicle to have complete systems, the patent reduces individual vehicle costs while maintaining overall system accuracy through coordinated operation.
3Productivity
If the system provides detailed and time-sensitive control instructions to individual vehicles, then vehicle operation efficiency is improved, but communication data volume and processing load increase
Solution Approach 1:
The patent segments the control system into hierarchical levels: cloud information services handle strategic planning and long-term predictions, traffic control centers manage regional coordination, and roadside units provide local real-time control. This segmentation allows detailed instructions to be generated only where needed (at the roadside level) while higher levels handle aggregate data, reducing overall communication volume.
Solution Approach 2:
The patent implements preliminary prediction and planning at higher system levels (cloud and TCC) before vehicles reach specific locations. By pre-computing route guidance, traffic flow predictions, and coordination strategies in advance, the system reduces the amount of real-time data that needs to be transmitted and processed during critical control moments.
Data Source
AI summary
The technology described herein provides systems and methods for an Autonomous Vehicle Cloud Control System (AVCCS) with a World Model. The AVCCS comprises a cloud-based platform, a communication module, and/or an onboard unit (OBU). The AVCCS leverages generative models, predictive models, and reinforcement learning methods to generate and synthesize comprehensive information at real-time, short-term, and long-term scales for sensing, transportation behavior prediction and management, planning and decision-making, and/or vehicle control. The comprehensive information generated from the World Model comprises vehicle surrounding information, weather information, vehicle attribute data, traffic state information, road information, and incident information. Additionally, the AVCCS is configured to provide one or more of data fusion, sensing, prediction, planning, decision-making, and control functions to generate detailed customized information at microscopic, mesoscopic, and macroscopic levels, and to generate time-sensitive control instructions for vehicles to fulfill driving tasks and provide operations and maintenance services.