AR Road Display Using Shared Sensor Data in Low Visibility
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Solution Overview
Problem
Existing vehicle systems lack effective methods to provide augmented reality views of road conditions during adverse weather or visibility issues, and fail to adequately monitor and assist drivers in dangerous situations.
Innovation Solution
Implementing an AI model that processes previously-captured sensor data from other vehicles to generate an augmented reality view of the road and integrates an AI assistant to monitor driver behavior, providing real-time assistance and control when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is processed in real-time during adverse weather conditions, then driver safety is improved, but system response time is insufficient due to data quality limitations
Solution Approach 1:
The system performs preliminary actions by collecting and storing sensor data from multiple vehicles during normal conditions before adverse weather occurs. This pre-collected data serves as a baseline for comparison and augmentation during poor visibility conditions, eliminating the need to wait for real-time data quality to improve.
Solution Approach 2:
The system introduces an intermediary mechanism by using sensor data from other vehicles as a mediator to supplement and augment the current vehicle's sensor data. This allows the system to overcome limitations of real-time sensor data quality by incorporating external data sources that provide additional contextual information about road conditions.
2Illumination intensity
If augmented reality display is implemented to show road views, then driver visibility is improved, but device complexity increases
Solution Approach 1:
The system applies universality by using a single augmented reality display component to serve multiple functions: displaying real-time sensor data, showing augmented road views from other vehicles, presenting weather condition information, and providing navigation guidance. This multi-functionality reduces the need for separate display systems for each function.
Solution Approach 2:
The system merges multiple data streams from different vehicles and sensors into a single integrated augmented reality display. By combining sensor data, road view images, weather information, and navigation data into one unified interface, the system improves driver visibility without requiring multiple separate display systems.
3Measurement precision
If AI model processes data from multiple vehicles to generate road views, then view accuracy is improved, but computational requirements increase
Solution Approach 1:
The system segments the computational workload by dividing the processing of multi-vehicle sensor data into discrete, manageable tasks. The AI model processes data from individual vehicles separately and then integrates the results, rather than processing all data simultaneously. This segmentation reduces peak computational energy requirements while maintaining view accuracy.
Data Source
AI summary
An example operation includes one or more of receiving sensor data of a vehicle as the vehicle travels along a road, determining that an environment around the vehicle has deteriorated based on the sensor data of the vehicle, extracting previously-captured sensor data of the road, wherein the previously-captured sensor data is captured by one or more other vehicles while travelling along the road, determining a view of the road based on execution of an artificial intelligence (AI) model on the previously-captured sensor data, and displaying the view of the road within the vehicle via augmented reality while the vehicle travels along the road.