Moving Object Recognition Using Roadside-Based Direction Estimation
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Solution Overview
Problem
Existing techniques for predicting the movement of a moving object around a vehicle have low responsiveness and accuracy due to the requirement for multiple positional information detections over a predetermined time period, which affects the initial value input to prediction filters.
Innovation Solution
A moving object recognition apparatus that includes a millimeter-wave radar for detecting moving objects and roadside objects, a position detection section to determine relative positions, a road direction estimation section to estimate road directions based on roadside objects, and a moving direction estimation section to estimate the moving direction using the road direction and relative speed, allowing for high responsiveness and accuracy in predicting movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple positional information detections are performed over a predetermined time period for prediction, then the accuracy of the initial value input to prediction filter is improved, but the responsiveness of predicting movement is deteriorated
Solution Approach 1:
The patent introduces roadside objects as intermediary reference points to estimate road direction. By using the positions of multiple roadside objects detected in a single detection process, the system can calculate the road direction without requiring multiple detections of the moving object itself, thus improving responsiveness while maintaining accuracy through the intermediary roadside object data
Solution Approach 2:
The system performs preliminary estimation of road direction using roadside object positions before performing prediction filter processing. This preliminary action provides an accurate initial value for the prediction filter based on a single detection process, eliminating the need for multiple sequential detections and thereby improving responsiveness
2Reliability
If multiple positional information detections are performed over a predetermined time period, then the reliability of prediction is improved, but the complexity of detection process is increased
Solution Approach 1:
The patent uses roadside objects as mediators to simplify the detection process. Instead of performing multiple complex detections of the moving object over time, the system performs a single detection process that captures both the moving object and multiple roadside objects, then uses geometric relationships to reliably estimate road direction and provide accurate initial values for prediction
Solution Approach 2:
The detection process is segmented into independent components: detecting roadside objects to estimate road direction, detecting the moving object to obtain its position, and then inputting these separately obtained data into the prediction filter. This segmentation allows each component to be processed independently in a single detection frame, reducing overall process complexity while maintaining reliability
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 apparatus enables high responsiveness and accuracy in estimating the moving direction of a moving object by calculating the road direction and moving direction using a single detection process, ensuring effective prediction of the object's path while minimizing the number of detections required.
Implementation Method 1
an object detection section that is configured to detect a moving object that moves on a road around an own vehicle and a roadside object by the road, from objects present around the own vehicle
Data Source
AI summary
A moving object recognition apparatus includes an object detection section, a position detection section, a road direction estimation section, and a moving direction estimation section. The object detection section detects a moving object that moves on a road around an own vehicle and a roadside object by the road, from objects present around the own vehicle. The position detection section detects positions of the moving object and the roadside object detected by the object detection section. The road direction estimation section estimates a road direction of the road on which the moving object is moving, based on the position of the roadside object detected by the position detection section. The moving direction estimation section estimates a moving direction of the moving object based on the road direction estimated by the road direction estimation section.