Occluded Vehicle Detection Using Road Illumination Cues
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Solution Overview
Problem
Self-driving vehicles face challenges in detecting occluded objects or road users, particularly in low-visibility conditions, which can limit their ability to take appropriate driving actions.
Innovation Solution
The self-driving vehicle employs a perception system that detects illumination from occluded objects, such as headlights, and uses this information to infer the presence, type, and characteristics of the occluded objects, allowing the vehicle to adjust its operational behavior accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the self-driving vehicle relies on direct line-of-sight detection, then the detection system is simple, but it cannot detect occluded objects in low-visibility conditions
Solution Approach 1:
The patent uses illumination patterns on the road surface as an intermediary indicator to infer the presence of occluded objects. Instead of directly detecting the occluded object, the system detects the light patterns cast by the object's headlights or taillights on the road, which serve as a mediator to reveal hidden objects indirectly.
Solution Approach 2:
The patent replaces traditional mechanical/optical direct detection methods with illumination pattern analysis. By analyzing the patterns of light on the road surface using image processing and computational algorithms, the system substitutes direct line-of-sight mechanical detection with a computational approach that can penetrate occlusions.
2Difficulty of detecting and measuring
If the vehicle uses illumination detection to identify occluded objects, then detection capability in low-visibility conditions improves, but the complexity of analyzing and interpreting illumination patterns increases
Solution Approach 1:
The patent segments the illumination detection task into distinct components: detecting light sources, analyzing illumination patterns on the road surface, identifying characteristic patterns (such as headlight pairs or taillight configurations), and inferring object properties. This segmentation breaks down the complex analysis into manageable stages that can be processed systematically.
Solution Approach 2:
The system performs preliminary analysis of illumination patterns to identify characteristic configurations before making final object identification. By pre-processing the illumination data to recognize patterns such as paired lights, their relative positions, and intensity distributions, the system reduces the complexity of subsequent interpretation steps.
3Reliability
If the vehicle modifies its operational behavior based on detected occluded objects, then driving safety improves, but the responsiveness and speed of decision-making may be reduced
Solution Approach 1:
The system performs preliminary detection and classification of occluded objects using illumination patterns before full decision-making is required. By pre-identifying the presence and basic characteristics of occluded objects through illumination analysis, the system prepares safety-critical information in advance, enabling faster response when action is needed.
Solution Approach 2:
The system continuously monitors illumination patterns and provides feedback to the operational control system. This ongoing feedback loop allows the vehicle to maintain awareness of occluded objects and adjust its behavior dynamically, balancing safety considerations with responsive decision-making based on real-time conditions.
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
This approach enables the self-driving vehicle to effectively detect and respond to occluded objects, even in low-visibility conditions, thereby enhancing its operational safety and efficiency.
Implementation Method 1
obtain, by one or more sensors of a perception system of the vehicle, illumination sensor data from an external environment around the vehicle
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.