Adverse-Weather Intelligent Driving With Roadside Perception
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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, supported by real-time communication and power supply networks, to facilitate vehicle operations and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing autonomous vehicles use complicated on-board systems, then vehicle control capability is improved, but system cost and complexity increase
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 infrastructure and vehicles, providing autonomous driving capabilities without requiring complex on-board sensor suites in each vehicle. The roadside units handle environmental perception and control decision-making, thereby reducing the automation burden on individual vehicle systems.
Solution Approach 2:
The patent extracts the sensing and control functions from the vehicle on-board systems and relocates them to roadside infrastructure units. By taking out the complex perception and decision-making capabilities from the vehicles and placing them in the infrastructure, the system achieves autonomous vehicle control while significantly reducing the complexity and cost of individual vehicle systems.
2Extent of automation
If existing autonomous vehicles use expensive on-board systems, then vehicle control capability is improved, but commercial implementation cost increases
Solution Approach 1:
The roadside units serve as cost-effective intermediaries that provide advanced autonomous driving capabilities without requiring expensive on-board sensor suites in each vehicle. By centralizing the expensive sensing and processing infrastructure in shared roadside units rather than duplicating them in every vehicle, the system achieves high-level automation at lower per-vehicle cost, enabling broader commercial deployment.
Solution Approach 2:
The roadside units are designed as universal infrastructure components that can serve multiple vehicles simultaneously. A single roadside unit can provide sensing, detection, and control services to numerous vehicles in its coverage area, thereby amortizing the infrastructure cost across many users and significantly reducing the cost burden for commercial implementation compared to equipping each vehicle with independent expensive systems.
3Productivity
If IRIS provides customized real-time control instructions, then vehicle operation efficiency is improved, but communication data requirements increase
Solution Approach 1:
The patent implements local quality by providing customized control instructions tailored to each vehicle's specific situation, location, and operational context. Rather than broadcasting generic information to all vehicles, the roadside units analyze individual vehicle states and environmental conditions to generate targeted control recommendations. This approach maximizes operational efficiency for each vehicle while minimizing unnecessary data transmission by sending only the specific information relevant to that vehicle's current needs.
Data Source
AI summary
The technology described herein provides an Intelligent Driving System for Adverse Weather Conditions (IDS-AWC) to enhance the safety and efficiency of autonomous vehicles (AVs). The system comprises an onboard unit (OBU) and/or a cloud platform, which integrate multi-source weather and environmental information from vehicle sensors, AVs, roadside units (RSUs), cloud platforms, and/or traffic control centers/traffic control units (TCC/TCU). The OBU processes data using learning-based, statistical, and empirical models to optimize vehicle control. The IDS-AWC improves situational awareness with high-definition maps for lane and road geometry recognition in low visibility and applies weather-adaptive control strategies, such as speed adjustments on slippery or icy roads. The cloud platform provides vehicle-specific weather forecasts and planning outputs to enhance decision-making. By integrating real-time perception, predictive analytics, and adaptive control, the IDS-AWC enhances AV robustness in rain, snow, fog, storm, and sandstorms, ensuring safer and more reliable operations under adverse weather conditions.