Roadside AI Server for Vehicle Perception and Localization
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Solution Overview
Problem
Autonomous and legacy vehicles face limitations in sensing capabilities, computing power, and positioning information due to limited on-board sensors and computing resources, which restrict their performance and decision-making abilities.
Innovation Solution
An on-demand roadside AI service system that utilizes a roadside server equipped with sensors and computing resources to receive requests from vehicles, process data, and transmit customized service messages for perception, localization, and decision services, enhancing sensing and computing capabilities without the need for increased on-board resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles rely solely on on-board sensors and computing resources, then device complexity is reduced, but sensing capabilities and computing power are insufficient
Solution Approach 1:
The patent introduces a roadside server as an intermediary between vehicles and the cloud infrastructure. This server provides sensing capabilities and computing power to vehicles without requiring these resources to be installed on-board. The roadside server processes sensor data from multiple sources and delivers processed information to vehicles, thereby improving reliability while avoiding the complexity of equipping each vehicle with extensive on-board resources.
Solution Approach 2:
The roadside server serves multiple vehicles simultaneously and performs multiple functions including data collection, processing, fusion, and distribution. It acts as a universal resource that can support various vehicle types (autonomous, connected, legacy) with different capability levels, providing a multi-functional solution that improves sensing capabilities across the board without requiring each vehicle to have specialized on-board equipment.
2Measurement precision
If vehicles are equipped with more on-board sensors and computing resources, then perception and decision-making accuracy is improved, but cost and device complexity increase
Solution Approach 1:
The roadside server acts as an intermediary that performs complex data processing, sensor fusion, and environmental modeling tasks externally. This allows vehicles to achieve high perception accuracy by receiving processed information from the roadside server without needing to equip themselves with expensive, complex on-board sensors and computing hardware for these specific functions.
Solution Approach 2:
Instead of each vehicle having its own complete sensing and processing system, the roadside server creates a virtual representation of the environment by fusing data from multiple sources including vehicle sensors, roadside sensors, and maps. This environmental model is then distributed to vehicles, allowing them to achieve accurate perception without duplicating the expensive sensing and processing infrastructure.
3Adaptability or versatility
If legacy vehicles with limited on-board resources use the roadside AI service, then service capability is improved, but network dependency increases
Solution Approach 1:
The roadside server serves as a necessary intermediary for legacy vehicles to access advanced AI services. It bridges the gap between legacy vehicles with limited on-board capabilities and the cloud-based AI infrastructure, providing a localized access point that enhances service capability while managing network dependencies through edge computing architecture.
Solution Approach 2:
The patent introduces a spatial dimension to the architecture by deploying roadside servers at strategic locations along the road network. This transforms the traditionally centralized cloud-based service into a distributed edge computing architecture, where services are delivered from nearby roadside servers rather than distant cloud data centers, reducing network latency and improving adaptability for legacy vehicles.
Data Source
AI summary
A method comprises receiving a service request from a vehicle, obtaining environment data with one or more sensors, determining a vehicle type of the vehicle based on the service request, determining service data responsive to the service request based on the vehicle type of the vehicle and the environment data, and transmitting a service message comprising the service data to the vehicle.