Driving-Task Sensor Classification for Real-Time Vehicle Monitoring
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Solution Overview
Problem
Conventional vehicle monitoring systems face challenges in processing large amounts of data from multiple sensors in real-time, especially in complex traffic situations, leading to unreliable detection of relevant objects, and require significant computational resources.
Innovation Solution
A monitoring system that classifies sensors into subsets based on the driving task, prioritizing sensor data processing to allocate computational resources efficiently, allowing for situational-dependent processing and reduced processing of less critical regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are processed in parallel to improve situational awareness, then the coverage and detection capability are improved, but the computational resources and processing time are significantly increased
Solution Approach 1:
The patent segments the sensor group into multiple subsets based on their relevance to the current driving task. The classification module divides sensors into first subset (highly relevant), second subset (moderately relevant), and third subset (less relevant). This segmentation allows the processing module to handle only the necessary subsets, reducing computational complexity while maintaining detection reliability for critical sensors.
Solution Approach 2:
The patent applies local quality by assigning different processing priorities to different sensor subsets based on their specific relevance to the driving task. Sensors in the first subset receive full processing priority, while sensors in lower-priority subsets receive reduced processing. This differential treatment optimizes computational resource allocation according to the specific needs of each sensor group.
2Reliability
If all sensors are processed with equal priority to ensure comprehensive monitoring, then the coverage is improved, but the processing time and computational load increase
Solution Approach 1:
The patent implements dynamic priority assignment where the classification of sensors into subsets is not fixed but changes based on the current driving task and situational context. The classification module continuously evaluates sensor relevance and adjusts subset assignments dynamically, allowing the system to adapt processing priorities in real-time according to changing driving conditions, thereby reducing processing time while maintaining monitoring reliability.
Solution Approach 2:
The patent changes the processing parameter (priority level) of different sensor subsets based on the driving task requirements. By modifying the processing priority parameter dynamically, the system can focus computational resources on critical sensors during specific driving scenarios, reducing overall processing time while ensuring that essential monitoring functions maintain adequate reliability.
3Area of stationary object
If more sensors are installed to improve situational awareness, then the coverage area is increased, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts and processes only the essential sensor data required for the current driving task. By using the classification module to identify and separate highly relevant sensors (first subset) from less relevant ones, the system extracts only the critical data streams that need full processing attention. This extraction approach allows comprehensive sensor coverage to be maintained physically, while computationally only the necessary portion is fully processed, reducing data processing complexity.
4Measurement precision
If sensor data from all regions is processed with high priority, then the detection accuracy is improved, but the computational resources required increase
Solution Approach 1:
The patent applies partial action by providing high-priority processing only to sensor subsets that are partially sufficient for the current driving task, rather than processing all sensors with equal high priority. The classification module identifies the minimum necessary subset of sensors (first subset) that provides adequate detection accuracy for the specific driving context. This partial processing approach maintains sufficient detection accuracy while significantly reducing computational resource consumption compared to processing all sensors at full priority.
Data Source
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AI summary
A monitoring system (100) for an environment of a vehicle (10) is disclosed. The vehicle (10) comprises a group of sensors (20) adapted to cover various regions of the environment. The monitoring system (100) includes a receiving module (110), a classification module (120), and a processing module (130). The receiving module (110) is adapted to obtain information (115) about a driving task of the vehicle (10). The classification module (120) is adapted to classify, based on the obtained information (115), the group of sensors (20) in at least a first subset (21) and a second subset (22). The processing module (130) is adapted to process sensor data of the first subset (21) with higher priority than sensor data from sensors from the second subset (22).