Stereo Vision System for Vulnerable Road User Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to effectively protect vulnerable road users from collisions with vehicles, resulting in significant injuries and fatalities, as they lack efficient detection and response mechanisms to prevent or mitigate such incidents.
Innovation Solution
A vulnerable road user protection system incorporating a stereo vision system that uses stereo cameras and processors to detect and recognize three-dimensional objects, providing alerts or automatic braking to prevent collisions, and deploying protective devices like external airbags or hood actuators to absorb impact energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a stereo vision system is implemented to detect vulnerable road users, then detection accuracy and response time are improved, but device complexity increases
Solution Approach 1:
The system segments the detection task by using multiple stereo camera pairs positioned at different locations (front, side, rear) to cover different viewing angles and distances. Each camera pair independently processes a specific spatial zone, allowing the complex detection problem to be divided into manageable segments that can be processed in parallel.
Solution Approach 2:
The system transitions from two-dimensional image processing to three-dimensional spatial understanding by utilizing stereo vision technology. Multiple camera pairs capture images from different spatial positions, enabling the construction of 3D models of vulnerable road users and accurate determination of their positions, velocities, and trajectories in three-dimensional space.
2Adaptability or versatility
If multiple stereo camera pairs are used to cover all viewing angles, then detection coverage is improved, but cost and device complexity increase
Solution Approach 1:
Each stereo camera pair is designed to serve multiple functions: detecting vulnerable road users, determining their three-dimensional positions, calculating velocities through frame-to-frame comparison, and identifying collision risks. This multi-functionality reduces the need for separate specialized sensors for each detection task.
Solution Approach 2:
The system merges the functions of multiple camera pairs by having them all feed into a centralized processing system that integrates data from all sources. The processor combines information from front, side, and rear cameras to create a comprehensive view of the environment, eliminating detection gaps without requiring every possible angle to be covered by dedicated specialized sensors.
3Speed
If real-time processing of stereo images is performed to detect vulnerable road users, then response time is improved, but computational energy consumption increases
Solution Approach 1:
The system performs preliminary actions by continuously capturing and pre-processing stereo images even before a vulnerable road user is detected. Image frames are captured at high frequency and pre-processed (alignment, feature extraction) in advance, so that when a vulnerable road user appears, the processing is already partially complete, reducing the critical response time.
Solution Approach 2:
The processing system implements selective intensity based on detected needs. During normal conditions, processing operates at standard intensity. When motion detection or other indicators suggest a vulnerable road user may be present, the system rushes through processing by increasing frame rate, using simplified algorithms, or prioritizing certain processing tasks to ensure rapid detection and response.
Data Source
AI summary
An image of a visual scene, comprising a plurality of pixels. is acquired and an associated range map is either determined therefrom or separately acquired. Elements of the range map comprise distances from the camera for each pixel of the image. In one aspect, either the image or the range map is processed with a connected-components sieve filter that locates clusters of pixels or elements that are connected to one another along either adjacent rows, columns or diagonally. In another aspect, a cross-range value of range-map element is compared with a down-range-responsive cross-range threshold of a boundary of a collision-possible space and the pixel or element is nulled or ignored if associated with a location that is not in the collision-possible space. The collision-possible space is responsive to an operating condition of a vehicle from which the image is acquired.