Vehicle Vision Shadow Detection via Light Source Positioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vehicle navigation systems based on camera sensors often incorrectly detect shadows as objects, leading to unsafe vehicle operations such as unnecessary braking, due to the inability to distinguish between physical objects and shadows.
Innovation Solution
A computer system in the vehicle uses image data from multiple cameras, light source determination, and positional analysis to differentiate between physical objects and shadows, avoiding evasive actions for shadows and ensuring safe navigation by determining the presence of a light source and the position of the sun or other light sources relative to detected objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle uses camera sensors to detect objects for collision avoidance, then the vehicle can identify obstacles and perform safety operations, but false detections occur when shadows are misinterpreted as objects
Solution Approach 1:
The system introduces an intermediary analysis layer that examines multiple attributes (luminance, shape, position, motion) of detected objects to distinguish shadows from real objects. This intermediary verification process prevents false detections by cross-checking camera data against shadow characteristics before triggering collision avoidance operations.
Solution Approach 2:
The system moves beyond simple 2D image detection by incorporating temporal dimension (motion analysis across frames) and spatial dimension (position relative to light sources and ground contact points). This multi-dimensional analysis enables the system to differentiate shadows from objects by examining their behavior across multiple dimensions rather than relying solely on static image interpretation.
2Reliability
If the vehicle navigates to avoid all detected objects, then collision avoidance is improved, but unnecessary evasive actions are taken when shadows are detected
Solution Approach 1:
The system implements feedback mechanisms where the detected object's characteristics are continuously analyzed and compared against shadow profiles. When an object is detected, the system feedback-loops through multiple verification checks (luminance ratio, shape consistency, motion patterns) to confirm whether evasive action is necessary, preventing unnecessary maneuvers while maintaining safe collision avoidance.
3Measurement precision
If the system analyzes multiple image attributes to distinguish shadows from objects, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The detection system is segmented into specialized modules: luminance analysis module, shape extraction module, position calculation module, and motion analysis module. Each module handles a specific aspect of shadow detection, allowing the complex analysis to be divided into manageable, optimized components that can be processed efficiently rather than as a monolithic complex system.
Data Source
AI summary
A computer is programmed to detect an object based on vehicle camera image data. The computer determines a light source and determines, based in part on a light source position, that the detected object is a shadow. The computer then navigates the vehicle without avoiding the object upon determining that the detected object is a shadow.