Occluded Object Detection Using Reflected Road Illumination
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Solution Overview
Problem
Self-driving vehicles face challenges in detecting occluded objects, particularly in low-visibility conditions, which limits their ability to adjust driving behavior effectively.
Innovation Solution
The vehicle's perception system detects illumination from external sources, such as headlights, and analyzes this data to identify and classify occluded objects, determining their type, characteristics, and behavior, using sensors like lidar, radar, and cameras to adjust driving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the self-driving vehicle uses traditional sensors to detect objects, then it can identify visible objects in the environment, but it fails to detect occluded objects or objects in low-visibility conditions
Solution Approach 1:
The patent uses light reflections from occluded objects as an intermediary signal to detect the presence of objects that are not directly visible. The perception system captures illumination patterns reflected from surfaces like roadways, signage, or other objects, which serve as mediators to indirectly detect occluded vehicles or road users without requiring direct line-of-sight to the objects themselves.
2Ease of operation
If the vehicle operates in autonomous mode without detecting occluded objects, then it can maintain simple driving operations, but it cannot safely adjust driving behavior to account for hidden road users
Solution Approach 1:
The perception system performs preliminary detection of occluded objects by analyzing illumination patterns before the vehicle reaches positions where direct detection would be possible. This advance detection allows the autonomous driving system to proactively adjust its behavior, such as reducing speed or changing lanes, to account for hidden road users before they become direct obstacles.
3Loss of information
If the perception system analyzes illumination data to identify occluded objects, then it can detect hidden objects and their characteristics, but it increases the complexity of data processing and analysis
Solution Approach 1:
The perception system segments the illumination analysis into distinct processing stages: first capturing raw illumination sensor data, then identifying light sources and their characteristics, next determining occluded object properties, and finally integrating this information into driving decisions. This segmentation of the complex analysis process into manageable steps reduces overall system complexity while maintaining comprehensive information extraction.
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
Enables the vehicle to safely navigate by detecting and responding to occluded objects, enhancing safety and operational efficiency in low-visibility conditions.
Implementation Method 1
Illumination by the other object of one or more areas in the surrounding area, for instance by its headlights, is detected by a perception system of the self-driving vehicle. This illumination can include reflections off of the roadway, signage or other objects.
Data Source
AI summary
The technology relates to detection of a nearby occluded object based on illumination emitted from that object. Illumination by the occluded object of one or more areas in the surrounding area, for instance by headlights of an occluded vehicle, is detected by a perception system of a self-driving vehicle. The self-driving vehicle can classify the detected object to determine whether the illumination is caused by a vehicle or other road user, or from objects in the surrounding environment. Illumination data and other information can be evaluated by the self-driving vehicle, for instance to identify a type of the object, a location of the object along a roadway, to disambiguate the direction of travel of the other object, etc. As a result, the self-driving vehicle may infer the behavior of the other object and modify its own driving operations to account for the other object's presence and likely behavior