Vehicle Object Tracking Using Grid Map and Occupancy Fusion
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Solution Overview
Problem
Existing autonomous vehicle technologies face limitations in object detection performance, particularly in recognizing objects not detected by deep learning methods.
Innovation Solution
An object tracking apparatus and method that utilize a sensor device to obtain surrounding vehicle information, generate a grid map, and employ deep learning to classify objects, while also detecting occupancy grids and clustering them to fuse with classification objects for enhanced tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning is used for object detection, then object recognition can be performed, but objects not detected by deep learning cannot be recognized
Solution Approach 1:
The patent merges deep learning-based classification objects with grid map-based occupancy objects to create fused tracking objects. This combination allows the system to detect both objects recognized by deep learning and objects missed by deep learning but visible in the grid map, thereby improving both recognition reliability and detection coverage.
Solution Approach 2:
The grid map serves as an intermediary between sensor data and object recognition. By generating occupancy grids from sensor information and comparing them with deep learning results, the system identifies objects that deep learning missed, expanding detection coverage while maintaining recognition accuracy.
2Reliability
If multiple processing methods are combined, then object recognition performance is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the object recognition process into distinct modules: deep learning classification, grid map generation, occupancy detection, and fusion processing. Each module handles a specific task independently, making the complex system more manageable and easier to implement while achieving enhanced recognition performance through their integration.
Data Source
AI summary
An object tracking apparatus and method are provided. The object tracking apparatus includes a sensor device that obtains surrounding information of a vehicle and a processor that tracks an object outside the vehicle based on the surrounding information obtained by the sensor device. The processor generates a grid map based on the surrounding information, deep-learns the grid map to obtain a classification object, detects an occupancy grid from the grid map and obtains a grid object based on clustering the occupancy grid, and fuses the classification object with the grid object to track the object.