Parking Sensor Fusion Grid Mapping for Low-Load Obstacle Detection
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Solution Overview
Problem
Conventional sensor fusion methods for parking assistance systems in transportation apparatuses face high computational load due to complex mathematical equations, leading to processor overload and difficulty in enhancing performance when fusing ultrasonic and camera sensor data.
Innovation Solution
A sensor fusion apparatus and method using a grid map for preprocessing and clustering, which includes Time of Flight preprocessing for ultrasonic sensors and object detection preprocessing for camera sensors, followed by clustering using an integrated grid map to reduce computational load and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Probabilistic Data Association Filter (PDAF) is used for sensor fusion, then identification accuracy is improved, but computational load increases causing processor overload
Solution Approach 1:
The patent extracts and removes the complex PDAF mathematical equations from the sensor fusion process, replacing them with a simplified grid map-based clustering approach that maintains identification accuracy while eliminating excessive computational requirements
Solution Approach 2:
The patent changes the fundamental parameters of the sensor fusion method by transitioning from continuous probabilistic calculations to discrete grid-based occupancy scoring, fundamentally altering how sensor data is processed and fused
2Reliability
If conventional sensor fusion method is used, then obstacle detection capability is improved, but device complexity increases making it difficult to add new features
Solution Approach 1:
The patent segments the sensor fusion process into distinct modular components: ultrasonic preprocessing module, camera preprocessing module, grid map generation module, and clustering module. This segmentation maintains detection capability while reducing overall system complexity and enabling easier feature addition
Solution Approach 2:
The grid map structure serves multiple functions simultaneously: it represents obstacle occupancy, provides a common coordinate system for fusion, enables clustering operations, and supports ghost object detection, thereby reducing the need for separate specialized modules
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
The method reduces processor load and enhances identification accuracy by utilizing the field of view of ultrasonic sensors and the intersection area of direct waves, making the system robust against noise and improving overall performance.
Implementation Method 1
the first preprocessing module performs Time of Flight (TOF) preprocessing and grid mapping for an object detected through an ultrasonic sensor
Data Source
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Figure 3(a)~3(d)
AI summary
A sensor fusion apparatus for a transportation apparatus includes: at least two sensors having different characteristics; a first preprocessing module and a second preprocessing module performing grid mapping for each object through preprocessing, corresponding to the at least two sensors having different characteristics, respectively; and a processor performing sensor fusion through clustering using an integrated grid map for the each object grid-mapped by the first preprocessing module and the second preprocessing module.