Railroad Light Activation Detection for False-Positive Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting the status of railroad lights, leading to potential unsafe decisions due to false positives or false negatives, which can result in incorrect maneuvers at railroad crossings.
Innovation Solution
A method and system that utilize image processing and machine learning algorithms to determine the illumination status of railroad lights over time, calculating a confidence level to adjust the vehicle's trajectory and control actions based on this likelihood, thereby reducing the risk of false positives and improving detection reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based detection (LIDAR, radar, cameras) is used to detect railroad lights, then the vehicle can perceive the environment, but false positives or false negatives occur leading to inaccurate detection of railroad light status
Solution Approach 1:
The system dynamically adjusts the confidence threshold for detecting railroad light status based on the vehicle's distance from the crossing. As the vehicle approaches, the system requires higher confidence levels before making a determination, allowing for adaptive detection accuracy that accounts for changing observation conditions and reduces false positives.
Solution Approach 2:
The system continuously updates the confidence level as new image data is received and processes it through the neural network. This feedback mechanism allows the system to refine its detection accuracy over time, adjusting its certainty based on the accumulated evidence from multiple observations and the vehicle's approaching trajectory.
2Measurement precision
If the vehicle slows down or stops frequently to verify railroad light status, then detection accuracy improves, but travel time increases
Solution Approach 1:
The system begins processing image data and calculating confidence levels well before the vehicle reaches the railroad crossing. By initiating detection early and continuously updating confidence as the vehicle approaches, the system has sufficient time to make accurate determinations without requiring last-minute slowing or stopping.
Solution Approach 2:
The system changes the parameter of confidence threshold requirements based on the vehicle's distance from the crossing. At greater distances, lower confidence thresholds allow for earlier detection decisions, while closer to the crossing, higher thresholds ensure accuracy without requiring the vehicle to stop, thus optimizing both precision and time efficiency.
Data Source
AI summary
The technology relates to controlling a vehicle based on a railroad light's activation status. In one example, one or more processors receive images of a railroad light. The one or more processors determine, based on the images of the railroad light, the illumination status of a pair of lights of the railroad light over a period of time as the vehicle approaches the railroad light. The one or more processors determine based on the illumination status of the pair of lights, a confidence level, wherein the confidence level indicates the likelihood the railroad light is active. The vehicle is controlled as it approaches the railroad light based on the confidence level.