Intersection Right-of-Way Detection Using Multi-Camera Traffic Light Voting
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Solution Overview
Problem
Automated vehicles face challenges in reliably detecting traffic lights and determining safe driving actions due to camera limitations, obstructed lenses, processor constraints, and the need for additional infrastructure, leading to potential failures in traffic light detection and incorrect results.
Innovation Solution
A method using multiple cameras on an automated vehicle to detect traffic lights, apply object recognition engines to identify states, and generate driving instructions based on matching states across cameras, with a processor determining safe maneuvers through intersections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single camera is used for traffic light detection, then the system complexity is low, but the detection reliability is insufficient due to camera limitations and obstructed lenses
Solution Approach 1:
The patent divides the detection task across multiple cameras positioned at different locations on the vehicle. Each camera captures traffic light data from its own perspective, and the processor compares results from all cameras to reach a consensus determination, thereby improving reliability through distributed detection
Solution Approach 2:
The patent combines data from multiple independent camera systems into a unified detection result. The processor integrates information from all cameras and uses a voting mechanism to determine the final traffic light state, merging multiple detection streams into a single reliable output
2Measurement precision
If multiple cameras are deployed to improve detection reliability, then the traffic light state accuracy improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent implements a voting mechanism where traffic light state determination requires agreement from multiple cameras. By requiring a threshold number of matching detections (excessive action beyond a single camera), the system achieves higher measurement precision while managing complexity through defined decision rules
Solution Approach 2:
The system uses feedback from multiple camera detections to verify and confirm traffic light state. Each camera's detection result feeds into the processor, which compares and validates results before final determination, creating a feedback loop that enhances accuracy
3Reliability
If V2I communication infrastructure is used, then traffic light state information can be obtained directly, but the infrastructure complexity and expense increase
Solution Approach 1:
The vehicle performs its own traffic light detection using onboard cameras and processing systems. The vehicle independently captures, analyzes, and determines traffic light states without relying on external V2I communication infrastructure, making the system self-sufficient and avoiding infrastructure complexity
4Reliability
If additional processing hardware is added to improve detection robustness, then the detection accuracy improves, but the cost and system complexity increase
Solution Approach 1:
The existing processor in the automated vehicle performs multiple functions including traffic light detection, object recognition, and driving decision-making. By making the processor multi-functional rather than adding dedicated hardware, the system achieves improved detection robustness without proportionally increasing hardware complexity or cost
Data Source
AI summary
Embodiments include systems and methods for determining states of traffic lights and managing behavior of an automated vehicle approaching an intersection. An autonomy system applies an object recognition engine trained to recognize a traffic light, and identify and confirm the state of the traffic light. A first neural network trained for object detection recognizes a traffic light and defines a bounding box around the recognized traffic light. A second neural network receives the region of the image bounded by the box as constituting the traffic light and “reads” the light. The automated vehicle uses other information, such as states of pedestrian traffic lights, detection of objects in and near the intersection, and glare on one or more cameras, to supplement its determination of the right of way through the intersection. The autonomy system generates a driving instruction based on the traffic light combined.