Time-of-Flight Imaging for Autonomous Vehicle Cross-Talk Separation
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Solution Overview
Problem
Existing time-of-flight imaging systems for guiding autonomous movable objects fail to effectively differentiate between light reflections from the object being detected and those from other light-emitting objects, leading to noise and reduced performance in detection, mapping, and navigation.
Innovation Solution
A time-of-flight imaging system that utilizes a first light source to illuminate a field of detection and includes a light sensor capable of detecting both self-reflected light and externally-induced light from other objects, allowing for the generation of feedback signals to modify motion and reduce cross-talk, while also enabling communication and data exchange to optimize detection patterns and adapt to surrounding conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the light sensor detects all reflected light in the field of detection, then the detection coverage is improved, but the measurement precision deteriorates due to inability to differentiate between self-induced and externally-induced light
Solution Approach 1:
The patent segments the detected light into two distinct categories: self-induced reflected light (from the vehicle's own light source) and externally-induced light (from other vehicles' light sources). The light sensor separately processes these two types of light signals, allowing the system to maintain comprehensive detection coverage while ensuring measurement precision by using only the self-induced light for depth calculations.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that identifies and separates externally-induced light from self-induced light before depth calculation. This intermediary step prevents contamination of depth measurements by external light sources while preserving the ability to detect all objects in the field of view.
2Device complexity
If the imaging system uses a single light source for illumination, then the device complexity is reduced, but the reliability deteriorates due to cross-talk from other autonomous vehicles' light sources
Solution Approach 1:
The patent implements a feedback mechanism where the light sensor detects externally-induced light from other vehicles and provides this information back to the imaging system. The system then adapts by differentiating between self-induced and externally-induced light, allowing reliable operation in multi-vehicle environments without requiring complex multi-source illumination systems.
3Measurement precision
If the system differentiates between self-induced and externally-induced light, then the measurement precision is improved, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The patent performs preliminary classification of light sources before depth calculation, identifying which detected light originates from the vehicle's own light source versus external vehicles. This preliminary action is integrated into the existing imaging pipeline, adding minimal processing complexity while significantly improving depth map accuracy by excluding external light contamination.
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
The system enhances detection accuracy and reduces noise by differentiating between self-induced and externally-induced light, enabling improved navigation, collision avoidance, and collaborative scanning among autonomous vehicles, while also optimizing imaging settings and sharing spatial information for enhanced safety and efficiency.
Implementation Method 1
a first light source (125) to illuminate a first field of detection (151)... at least one light sensor (130) to detect reflected light. The detected reflected light comprises first reflected light (155) comprising light emitted by the first light source (125) reflected at the first field of detection (151)
Implementation Method 2
The imaging system (100) is arranged to determine a depth map of the first field of detection (151) based on the detected first reflected light (155)
Data Source
AI summary
A guiding system guides an autonomous movable object. The guiding system has a time-of-flight imaging system, which has a first light source to illuminate a first field of detection; and a light sensor. The light sensor detects first and second reflected light. The first reflected light has light emitted by the first light source reflected at the first field of detection. The second reflected light originates from a second field of detection illuminated by a second light source, which is independent from the first light source and is coupled to a different movable object. The imaging system differentiates between the first and second reflected light, determines a depth map of the first field of detection based on the detected first reflected light, and generates feedback. The imaging system has a motion controller that receives the feedback, and modifies a motion of the autonomous movable object based on the feedback.