Intersection Traffic Signal Mapping Under Low-Confidence Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and operating at intersections due to low-confidence object detection conditions, particularly when traffic flow control devices are partially or completely occluded, and rely heavily on high-definition maps that may be unavailable or incomplete.
Innovation Solution
A method and apparatus for operating a vehicle that involves receiving sensor data from vehicle-mounted sensors to detect traffic flow control devices, determining their type and state, and tagging them with labels including location, even when detection confidence is below a defined level, allowing the vehicle to operate using these labels within the vehicle transportation network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the vehicle relies on sensor data with high detection confidence to identify traffic flow control devices, then the accuracy of navigation decisions is improved, but the vehicle's ability to operate in low-confidence detection conditions deteriorates
Solution Approach 1:
The system performs preliminary annotation of traffic flow control devices by tagging them with labels including location, type, and state information before navigation decisions are required. This advance preparation allows the vehicle to have pre-processed, reliable data about traffic devices even when real-time sensor confidence is low, enabling safe operation in challenging detection conditions
Solution Approach 2:
The system introduces an intermediary annotation layer that mediates between raw sensor data and navigation decisions. By creating structured labels with location, type, and state information about traffic flow control devices, this intermediary representation provides reliable operational data even when direct sensor detection confidence is below thresholds, resolving the contradiction between measurement precision and operation reliability
2Measurement precision
If the vehicle uses detailed sensor data and processing to improve detection accuracy of traffic flow control devices, then navigation precision is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system extracts only the essential information needed for safe navigation by tagging traffic flow control devices with specific labels containing location, type, and state. This extraction approach focuses processing on critical attributes rather than analyzing complete sensor datasets, improving detection accuracy while reducing overall computational complexity by eliminating unnecessary processing of non-essential data
3Productivity
If the vehicle operates with low detection confidence thresholds to maintain continuous operation, then operational continuity is improved, but the risk of incorrect navigation decisions increases
Solution Approach 1:
The system performs preliminary annotation and tagging of traffic flow control devices with comprehensive labels before navigation decisions are required. This advance preparation creates a repository of reliable, pre-processed information that can be consulted during operation, allowing the vehicle to maintain continuous operation even when real-time detection confidence is low, without sacrificing navigation decision reliability
Solution Approach 2:
The system prepares annotated data about traffic flow control devices in advance, creating a buffer of reliable information that cushions against the risks of low-confidence real-time detection. This beforehand preparation ensures that even when operational continuity requires operating below ideal detection thresholds, the pre-annotated data provides a safety margin that prevents incorrect navigation decisions
Data Source
AI summary
A vehicle receives sensor data from at least one of its sensors as it approaches an intersection and determines whether a traffic flow control device for the intersection is detected. When detected, a detected type, a detected state, or both of the traffic flow control device is determined. Using a type of the intersection, at least one of an existing type or an existing state of the traffic flow control device is determined, where the traffic flow control device is undetected or the detected type, the detected state, or both are determined with a detection confidence less than a defined level of detection confidence. The traffic flow control device is tagged with a label including its location and existing type, the existing state, or both within at least one control system for the vehicle. The vehicle is operated within vehicle transportation network using a control system that incorporates the label.