Collaborative Vehicle Illumination for Road Hazard Visibility
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Solution Overview
Problem
Modern vehicles face challenges in effectively illuminating road hazards and vulnerable road users, as conventional adaptive forward lighting systems only consider the host vehicle's trajectory and forward geometry, lacking collaboration with other vehicles and IoT devices for enhanced perception and illumination.
Innovation Solution
The proposed solution involves a computer-implemented method and system for collaborative illumination, using data from multiple vehicles and IoT devices to identify and illuminate road hazards and vulnerable road users through connected vehicle telemetry and perception data, integrating edge and cloud computing environments to generate lighting patterns that enhance visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If conventional adaptive forward lighting systems are used, then the host vehicle's trajectory and forward geometry are illuminated, but road hazards and vulnerable road users are not adequately visible
Solution Approach 1:
The patent merges illumination capabilities across multiple vehicles and IoT devices to create a collaborative lighting network. The processing system integrates data from multiple sources and coordinates lighting actions across different vehicles to collectively illuminate road hazards and vulnerable road users, transforming individual lighting systems into a coordinated networked system that achieves broader coverage without requiring each vehicle to have complex individual capabilities.
Solution Approach 2:
The patent enables vehicles to perform multiple functions: they act as both sensors for detecting road hazards and vulnerable road users, and as illumination sources. The system processes telemetry and perception data to identify objects of interest, then coordinates lighting actions to illuminate these objects. This multi-functional approach allows the same vehicle infrastructure to serve both detection and illumination purposes, reducing the need for dedicated specialized equipment.
2Reliability
If collaborative illumination with multiple vehicles and IoT devices is implemented, then visibility of road hazards and vulnerable road users is enhanced, but system complexity increases
Solution Approach 1:
The patent introduces a communication network as an intermediary that coordinates between multiple vehicles and the central processing system. This intermediary manages the complexity of inter-vehicle communication by providing standardized data exchange protocols and coordination mechanisms. The network handles the orchestration of illumination actions across multiple vehicles, abstracting the complexity from individual vehicle systems while enabling reliable collaborative illumination for safety-critical applications.
3Difficulty of detecting and measuring
If conventional lighting systems are used, then the system is simple to operate, but road hazards and vulnerable road users remain difficult to detect
Solution Approach 1:
The patent implements self-service through automated processing that receives telemetry data and perception data, automatically identifies road hazards and vulnerable road users, and triggers illumination actions without requiring manual driver intervention. The system autonomously processes sensor data, determines objects of interest, and coordinates lighting responses, thereby improving detection capability while maintaining ease of operation through full automation of the complex detection and coordination tasks.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the visibility of road hazards and vulnerable road users by leveraging connected vehicle data, reducing the risk of accidents by making these obstacles more observable to drivers, thereby enhancing safety through collaborative illumination.
Implementation Method 1
causing the object to be illuminated
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes includes receiving, by a processing device of a vehicle, first road data. The method further includes receiving, by the processing device of the vehicle, second road data from a sensor associated with the vehicle. The method further includes identifying, by the processing device of the vehicle, an object based at least in part on the first road data and the second road data. The method further includes causing, by the processing device of the vehicle, the object to be illuminated.