Traffic Light Detection Pipeline for Autonomous Vehicle Vision
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and classifying traffic lights and other traffic indicators in images captured by onboard image-capture devices, particularly under varying environmental conditions, which can affect the vehicle's control systems.
Innovation Solution
An imaging system in autonomous vehicles uses a processing system to analyze image portions, calculate confidence scores, and employ a supervised machine-learning classifier to identify candidate traffic light portions, adjusting thresholds based on conditions, and provide control instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image processing methods are used to detect traffic lights, then the system complexity is low, but the detection accuracy and reliability deteriorate under varying environmental conditions
Solution Approach 1:
The system performs preliminary actions by dividing the image into multiple regions of interest before detailed analysis, and uses preliminary filtering to identify candidate traffic light locations. This staged approach improves detection reliability by preparing the data structure in advance while managing complexity through progressive refinement.
Solution Approach 2:
The image processing is segmented into distinct stages: initial image acquisition, region division, candidate identification, classification, and verification. Each stage handles specific aspects of detection, improving overall reliability by breaking down the complex task into manageable, specialized sub-tasks that can be optimized independently.
2Measurement precision
If multiple image processing stages are implemented to improve detection accuracy, then the detection precision improves, but the processing time increases
Solution Approach 1:
The system applies partial action by focusing processing resources on regions identified as potential traffic light locations rather than analyzing the entire image uniformly. Candidate regions undergo detailed multi-stage analysis while other regions receive minimal processing, achieving high detection precision for critical areas without proportionally increasing overall processing time.
Solution Approach 2:
The processing pipeline dynamically adjusts the level of analysis applied to different image regions based on their likelihood of containing traffic lights. Regions with higher probability receive more intensive multi-stage processing, while lower-probability regions receive streamlined processing, optimizing the balance between detection precision and processing time.
Data Source
AI summary
The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a plurality of image portions. The image processing system also calculates a score for each image portion. The score indicates a level of confidence that a given image portion represents an illuminated component of a traffic light. The image processing system further identifies one or more candidate portions from among the plurality of image portions. Additionally, the image processing system determines that a particular candidate portion represents an illuminated component of a traffic light using a classifier. Further, the image processing system provides instructions to control the autonomous vehicle based on the particular candidate portion representing an illuminated component of a traffic light.