Vehicle Interior Light Modules for Real-Time Sensor Visualization
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Solution Overview
Problem
Existing systems for visualizing sensor and measuring data in vehicles using light modules are limited by pre-stored patterns, making it impossible to react to real-time situations effectively, as they can only depict predefined scenarios and lack flexibility in responding to dynamic conditions.
Innovation Solution
The method involves detecting sensor data as video data using cameras or other sensors, analyzing it to extract relevant structures, and converting it into video sequences that can be displayed on light modules in real-time, allowing for dynamic and flexible visualization of current situations, including prioritization of data sources for enhanced safety and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-stored patterns are used for visualization, then device complexity is reduced and ease of operation is improved, but adaptability to real-time situations deteriorates
Solution Approach 1:
The system transitions from static pre-stored patterns to dynamic real-time video sequence generation. The central control device continuously processes sensor data and generates updated video sequences that adapt to changing traffic and vehicle situations, making the visualization system dynamic rather than static.
Solution Approach 2:
The system pre-processes and stores video sequences for common or repetitive situations in advance. When similar situations occur, these pre-stored sequences can be quickly retrieved and displayed, reducing real-time computational requirements while maintaining adaptability for novel situations.
2Productivity
If real-time video data processing is implemented, then adaptability to dynamic situations is improved, but computational requirements and processing time increase
Solution Approach 1:
Video sequences for common situations are pre-calculated and stored in advance. When the same or similar situations occur, the system retrieves these pre-computed sequences instead of processing raw sensor data in real-time, significantly reducing processing time while maintaining real-time responsiveness for novel situations.
Solution Approach 2:
The system applies different processing strategies to different situations: pre-stored sequences are used for common/repetitive situations to minimize processing time, while full real-time processing is applied only to novel or critical situations that require immediate adaptation.
3Measurement precision
If high-resolution video data is processed for detailed visualization, then measurement precision is improved, but data transmission and processing load increase
Solution Approach 1:
The system extracts only the essential and relevant structures from the video data that are necessary for effective visualization. Rather than transmitting or processing all video pixels, the system identifies and processes key features and structures that convey the most important information about traffic and vehicle situations.
Data Source
AI summary
A method for visualizing sensor data from the surroundings of a vehicle and/or measuring data from the vehicle uses light modules in the interior of the vehicle for the visualization of the sensor data. The sensor data is detected as video data, after which the video data is analyzed in relation to relevant recognizable structures, after which the relevant structures are transferred to a video sequence with a format fitting for the respective light module, and/or sensor data not detected as video data and/or measuring data is recalculated into video sequences via an algorithm, after which the video sequences from the different data are superimposed and displayed on the light modules.