Road Sign Detection Pipeline With Parallel Classifier Arbitration
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Solution Overview
Problem
Autonomous vehicles face challenges in quickly and accurately detecting and understanding various types of road signs, especially non-standard or region-specific signage, which can impact their operation and response times.
Innovation Solution
A unified pipeline approach incorporating a generic sign detection method with a robust parallel classification strategy, using sensor data from cameras and lidar to identify road signs, and a modular classification approach with trained machine learning classifiers, along with a text and symbol detector to arbitrate sign types, and optional remote assistance for unknown signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a generic sign detector is used to identify road signs, then detection speed is improved, but classification accuracy for diverse sign types deteriorates
Solution Approach 1:
The system segments the sign detection and classification process into two distinct stages: a generic sign detector that quickly identifies potential sign locations, and multiple specialized sign type classifiers that perform detailed classification on detected candidates. This segmentation allows the system to maintain high detection speed while achieving accurate classification through specialized models for each sign type.
Solution Approach 2:
The generic sign detector acts as an intermediary component that bridges the gap between raw sensor data and specialized classification models. It pre-processes sensor data to identify candidate sign regions, which are then passed to appropriate sign type classifiers. This intermediary stage enables efficient routing of data to the right classification models without requiring all classifiers to process all data.
2Measurement precision
If multiple specialized sign classifiers are used for accurate classification, then classification accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements a universal classification framework that handles multiple sign types through a common architecture. The generic sign detector serves as a universal entry point that routes candidates to appropriate specialized classifiers based on detected properties. This universal framework allows the system to maintain high classification accuracy for diverse sign types while managing complexity through standardized processing pipelines and property-based routing mechanisms.
3Reliability
If a robust parallel classification strategy is implemented, then classification robustness is improved, but processing time increases
Solution Approach 1:
The system employs periodic action by implementing a two-stage classification process where the generic sign detector performs initial filtering at high speed, and specialized sign type classifiers perform detailed classification only on detected candidates. This periodic application of classification stages ensures robust and accurate classification while minimizing overall processing time by avoiding unnecessary classification of non-sign objects.
Data Source
AI summary
The technology provides a sign detection and classification methodology. A unified pipeline approach incorporates generic sign detection with a robust parallel classification strategy. Sensor information such as camera imagery and lidar depth, intensity and height (elevation) information are applied to a sign detector module. This enables the system to detect the presence of a sign in a vehicle's external environment. A modular classification approach is applied to the detected sign. This includes selective application of one or more trained machine learning classifiers, as well as a text and symbol detector. Annotations help to tie the classification information together and to address any conflicts with different outputs from different classifiers. Identification of where the sign is in the vehicle's surrounding environment can provide contextual details. Identified signage can be associated with other objects in the vehicle's driving environment, which can be used to aid the vehicle in autonomous driving.