Localized Generative AI World Models for Vehicle Control
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Solution Overview
Problem
Existing autonomous vehicle technologies require expensive, complicated, and energy-inefficient on-board systems, relying heavily on vehicle sensors for control and management, which poses challenges in implementing automated vehicle systems.
Innovation Solution
An Intelligent Road Infrastructure System (IRIS) with roadside units (RSUs) equipped with AI capabilities for vehicle localization, object detection, behavior prediction, and traffic management, utilizing machine learning and decentralized intelligence coordination to enhance vehicle control and traffic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing autonomous vehicle technologies use on-board systems with multiple sensing systems, then vehicle control and management can be achieved, but the system becomes expensive, complicated, and energy-inefficient
Solution Approach 1:
The patent introduces roadside units (RSUs) as intermediary infrastructure components that perform sensing, detection, and computation functions externally. These RSUs act as mediators between the vehicle and the environment, providing localization, object detection, and behavior prediction data to vehicles without requiring complex on-board sensing systems. This shifts the computational and sensing burden from the vehicle to the infrastructure, resolving the contradiction between reliable vehicle control and reduced system complexity.
Solution Approach 2:
The patent replaces mechanical/sensor-based on-board detection systems with AI-based computational systems located in infrastructure. Instead of using multiple physical sensors and complex mechanical processing units in vehicles, the system uses AI models running on roadside infrastructure to perform sensing and analysis functions, then communicates results to vehicles. This substitution reduces on-board hardware complexity while maintaining control reliability.
2Reliability
If existing autonomous vehicle technologies rely heavily on vehicle sensors, then vehicle control can be achieved, but energy efficiency deteriorates
Solution Approach 1:
The roadside units serve as energy-efficient intermediaries that perform computationally intensive sensing and analysis tasks externally. By relocating these functions from the vehicle's power-constrained onboard systems to infrastructure-based RSUs with unlimited power supply, the system maintains reliable vehicle control while dramatically reducing the energy consumption required for sensing, detection, and decision-making operations.
Solution Approach 2:
The system creates virtual copies of sensing and detection capabilities through AI models running on infrastructure rather than requiring physical sensor copies in every vehicle. The RSUs generate digital representations of the environment, object states, and predictions that vehicles can use without duplicating expensive and energy-intensive sensing hardware, thereby reducing overall system energy consumption while maintaining control reliability.
3Reliability
If decentralized intelligence coordination is implemented in IRIS, then system robustness and performance are enhanced, but system complexity increases
Solution Approach 1:
The patent segments the intelligence coordination function into modular AI models and algorithms distributed across multiple roadside units. Each RSU operates independently with its own localized AI capabilities for sensing, detection, and prediction, while coordinating with other RSUs through standardized communication protocols. This segmentation enables decentralized robustness while managing complexity through modular, reusable components rather than monolithic system-wide coordination.
Solution Approach 2:
The patent implements universal AI models and communication protocols that can be deployed across all roadside units in the infrastructure. These multi-functional components handle multiple tasks including localization, object detection, behavior prediction, and inter-vehicle communication using the same hardware and software platform. This universality reduces the complexity of deploying and coordinating decentralized intelligence by using standardized, interchangeable building blocks rather than custom solutions for each RSU.
Data Source
AI summary
This technology provides an autonomous vehicle (AV) system that integrates a localized generative artificial intelligence (AI) system with a world model for automated vehicle control and traffic operations. The AI system comprises a machine learning component that uses historical and real-time environmental or road data to improve models and algorithms for identifying vehicles and objects and predicting vehicle movements. The AI system features an environment prediction component configured to generate road and environmental condition forecasts based on both historical and real-time information. The AI system is configured to generate numerous long-tail cases that are challenging or impractical to be collected directly from real-world scenarios, such as traffic accidents, adverse weather conditions, natural hazards, pavement breakdown, traffic events, and/or communication malfunction.