Neural Sensor Coordination for Vehicle Subregion Detection
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Solution Overview
Problem
Existing sensor systems in vehicles operate independently, leading to incomplete and inefficient environmental perception, particularly in areas outside the primary field of view, which can compromise automated driving systems.
Innovation Solution
A method utilizing a neural network to generate a control signal for a second sensor system based on data from a first sensor system, enhancing the detection of environmental sub-areas by actively directing the second sensor's focus, such as through angle, distance, and elevation adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors of different modalities are used independently, then each sensor can operate autonomously and process data individually, but the environmental perception becomes incomplete and inefficient, particularly in areas outside the primary field of view
Solution Approach 1:
The patent merges data from multiple independent sensors (video camera, radar, LIDAR) into a unified environmental perception system. The video data, radar reflections, and LIDAR measurements are combined to create a comprehensive view of the environment, particularly improving detection in areas outside the primary field of view through sensor fusion techniques.
Solution Approach 2:
The detection device is designed to perform multiple functions: it processes video data for visual perception, radar signals for range and velocity detection, and LIDAR data for precise distance measurement. This multi-functional approach allows a single system to handle diverse sensing tasks that would otherwise require separate dedicated systems.
2Area of stationary object
If a video camera detects the entire scene continuously, then complete environmental coverage is achieved, but the detection precision and attention to relevant areas are reduced compared to human visual perception
Solution Approach 1:
The system dynamically adjusts detection resources based on situational relevance. Instead of uniform continuous detection across the entire scene, the video camera and other sensors prioritize and enhance detection in areas of interest (such as regions with detected objects or potential hazards) while maintaining baseline monitoring of the broader environment, mimicking human selective attention.
Solution Approach 2:
The patent applies different detection qualities to different regions of the environment. High-resolution video and enhanced processing are applied to areas identified as relevant or containing objects of interest, while peripheral areas receive standard monitoring. This creates a quality gradient in detection precision that optimizes computational resources while maintaining safety.
3Ease of manufacture
If sensors process and link data in later steps individually, then initial processing is simple and independent, but the integration and association of data from different modalities becomes complex and less efficient
Solution Approach 1:
The system performs preliminary association and coordination of data from different sensors during the initial processing stage rather than in later steps. The detection device establishes correspondences between video pixels, radar reflections, and LIDAR measurements early in the processing pipeline, creating a unified representation that simplifies subsequent analysis and reduces integration time.
Data Source
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AI summary
A method for detecting an environment of a first sensor system is proposed, comprising the following steps: providing a temporal sequence of data of the first sensor system for detecting the environment; generating an input tensor using the temporal sequence of data of the first sensor system for a trained neural network, the neural network having been configured and trained to take the input tensor as a basis for identifying at least one subregion of the environment by means of a second sensor system in order to improve detection of the environment; generating a control signal for the second sensor system by means of an output signal of the trained neural network in order to improve detection of the environment in the at least one subregion.