Automated Driving Cloud Control for Long-Tail Corner Cases
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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 and management services through a network of roadside units, traffic control units, traffic control centers, vehicle onboard units, and cloud computing services, supported by real-time communication and power supply networks, to facilitate vehicle operations and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use expensive and complicated on-board systems, then vehicle control and sensing capabilities are improved, but system cost and complexity increase significantly
Solution Approach 1:
The patent introduces roadside units (RSUs) as intermediary components that perform sensing, detection, and control functions externally. These RSUs act as mediators between the vehicle and the infrastructure, providing autonomous vehicle capabilities without requiring complex on-board systems. The roadside units handle tasks such as object detection, traffic signal control, and vehicle guidance, thereby reducing the complexity burden on individual vehicles while maintaining high reliability.
Solution Approach 2:
The patent shifts the complexity from the vehicle dimension to the infrastructure dimension. Instead of each vehicle carrying complex sensing and control systems, the solution distributes these functions across a network of roadside units positioned along the roadway. This dimensional shift allows individual vehicles to have simpler onboard equipment while the collective infrastructure provides the necessary autonomous driving capabilities.
2Measurement precision
If autonomous vehicles use expensive and complicated on-board systems, then vehicle sensing and navigation are improved, but commercial implementation becomes difficult
Solution Approach 1:
The roadside units serve as external sensing intermediaries that provide high-precision environment detection capabilities. These RSUs are equipped with sensors, cameras, and detection devices that monitor the roadway, pedestrians, vehicles, and environmental conditions. By placing these sophisticated sensing systems in the infrastructure rather than in each vehicle, the patent achieves high measurement precision while reducing the cost and manufacturing complexity for individual vehicles.
Solution Approach 2:
The roadside units are designed as universal, multi-functional components that can serve multiple vehicles simultaneously. A single RSU can provide sensing, detection, and control services to numerous passing vehicles, making the system cost-effective and commercially viable. This shared infrastructure approach eliminates the need for each vehicle to have its own complete sensing system, thereby improving ease of manufacture and commercial implementation.
3Productivity
If the system provides customized real-time control instructions to individual vehicles, then vehicle operation efficiency is improved, but communication and data processing requirements increase
Solution Approach 1:
The patent implements local quality by providing customized control instructions tailored to each vehicle's specific needs, location, and operational context. Rather than broadcasting generic information to all vehicles, the system analyzes individual vehicle characteristics, destinations, and real-time conditions to deliver targeted guidance. This approach improves vehicle operation efficiency by optimizing each vehicle's path and behavior while managing data transmission through selective, relevant communication.
Solution Approach 2:
The system employs feedback mechanisms where roadside units continuously monitor vehicle responses to control instructions and adjust subsequent communications accordingly. This feedback loop allows the system to refine data transmission by sending only necessary updates and corrections, reducing overall data volume while maintaining high vehicle operation efficiency. The feedback mechanism ensures that customized instructions are optimized based on actual vehicle performance and changing conditions.
Data Source
AI summary
The technology described herein provides systems and methods for an Automated Driving Cloud System (ADCS) for long-tail corner cases. The ADCS for long-tail corner cases comprises a cloud-based platform, a communication module, and/or an onboard unit (OBU). The ADCS leverages world models to provide automated driving functions including sensing, prediction, planning, decision making, and control at microscopic, mesoscopic, and/or macroscopic levels. The system is specifically designed to address long-tail corner cases, which include work zones, special events, reduced speed zones, incident detection, buffer spaces, and adverse weather conditions. Additionally, the ADCS is configured to provide safety and efficiency measures for vehicle operations and control at various special scales that require additional system coverage, including construction zones, special event zones, and special weather conditions. The ADCS enables adaptive and reliable automated driving in highly uncertain and dynamic environments.