Vehicle Distance Estimation Using Virtual Object Orientation
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Solution Overview
Problem
Existing systems for estimating the distance to a target vehicle from a host vehicle face challenges in accuracy, especially when the target vehicle is inclined or far away, and require high annotation costs for training data.
Innovation Solution
An apparatus that uses a processor to input images from a camera into a classifier to detect and classify vehicle regions, then estimates distance by identifying the position of a virtual object with a standard length and width based on the vehicle type, using the ratio of front-back and side area widths to determine the virtual object's orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the object region includes side surface as well as back of target vehicle, then the classification can identify vehicle type, but the distance estimation accuracy deteriorates because the object region is wider than the back of target vehicle
Solution Approach 1:
The patent segments the vehicle detection task into two distinct parts: (1) vehicle type classification using the entire object region, and (2) distance estimation using only the back region. This segmentation allows each task to use the most appropriate features without interference, resolving the contradiction between comprehensive classification and precise distance measurement.
Solution Approach 2:
The patent extracts the back region from the complete object region specifically for distance estimation purposes. By taking out this specific portion, the system can perform accurate distance measurement using only the relevant area, while the full object region remains available for vehicle type classification.
2Area of stationary object
If the distance from host vehicle to target vehicle increases, then the coverage area increases, but it becomes difficult to identify the back of target vehicle from the image correctly
Solution Approach 1:
The patent introduces a virtual object as an intermediary that represents the target vehicle's back region in standardized form. This virtual object serves as a mediator between the actual distant vehicle (which appears small and difficult to identify) and the distance estimation algorithm, providing a consistent reference frame regardless of distance.
Solution Approach 2:
The patent changes the parameter representation from pixel-based measurements (which vary with distance) to standardized virtual object parameters. By transforming the detection problem into parameter space, the system can maintain consistent distance estimation accuracy across different distances.
3Measurement precision
If three-dimensional orientation is used to detect target vehicle, then the distance estimation accuracy improves, but the annotation cost of making training data becomes enormous
Solution Approach 1:
The patent replaces expensive 3D orientation annotations with simpler, cheaper 2D bounding box annotations. By using only basic rectangular region detection instead of complex three-dimensional parameter annotation, the system achieves acceptable distance estimation accuracy at a fraction of the data annotation cost.
Solution Approach 2:
The patent creates a simplified 2D copy (virtual object) of the target vehicle's back region instead of using full 3D orientation data. This copied representation captures the essential information needed for distance estimation while requiring minimal annotation effort.
Data Source
AI summary
An apparatus for estimating distance inputs an outside image representing the situation around a vehicle and obtained from a camera mounted on the vehicle into a classifier to detect a vehicle region representing a target vehicle and to classify the target vehicle represented in the detected vehicle region as one of preregistered types of vehicles. The apparatus then identifies the position of a virtual object corresponding to the target vehicle represented in the vehicle region, and estimates the distance from the vehicle to the virtual object as the distance from the vehicle to the target vehicle.