Predictive Sensor Gate Control for Autonomous At-Grade Crossings
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Solution Overview
Problem
Collecting and maintaining vehicle speed and position data for autonomous vehicles at at-grade crossings is complex and processing intensive, particularly due to the dynamic nature of vehicle positions and speeds, posing safety and operational challenges, especially when interacting with trains.
Innovation Solution
A system utilizing sensors along a dedicated roadway to monitor autonomous vehicles, predict their intent to access at-grade crossings, and manage access through gate systems, ensuring safe navigation and toll collection, while integrating with train operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors and gate systems are deployed to monitor and control autonomous vehicles at at-grade crossings, then safety and operational reliability are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system divides the roadway into multiple monitored zones with sensors positioned at specific locations (e.g., before and after at-grade crossings). The gate system is segmented into multiple controllable barriers that can be independently activated. This segmentation allows the complex safety monitoring function to be distributed across multiple simpler components rather than requiring a single complex centralized system.
Solution Approach 2:
The system performs preliminary actions by detecting autonomous vehicles before they reach the at-grade crossing and proactively controlling the gate system in advance. Sensors monitor vehicle approach and trigger gate activation before the vehicle reaches the crossing point, ensuring safety is established beforehand rather than reacting to potential hazards after they arise.
2Reliability
If real-time monitoring and prediction of vehicle intent is implemented, then safe navigation through at-grade crossings is improved, but processing intensity and computational requirements increase
Solution Approach 1:
The system applies partial action by focusing computational resources on specific critical zones (at-grade crossings) rather than processing all roadway data uniformly. The machine learning model analyzes only the subset of data relevant to crossing safety (vehicle position, speed, intent) rather than processing all possible vehicle parameters, achieving adequate safety monitoring with reduced processing intensity.
3Reliability
If gate systems are used to control access to at-grade crossings, then train-vehicle interaction safety is improved, but ease of operation for autonomous vehicles decreases
Solution Approach 1:
The gate system incorporates feedback mechanisms where sensors continuously monitor vehicle position and gate status. The system receives feedback about vehicle approach and adjusts gate activation accordingly. This feedback loop ensures the gate opens when safe and closes when trains are present, maintaining safety while allowing smooth vehicle operation through automated coordination.
Solution Approach 2:
The autonomous vehicles interact with the gate system through automated communication, where the vehicle's own sensor data and intent predictions trigger gate activation without requiring external manual control. The system serves itself by using the vehicle's own operational data to control the gate, reducing the burden on the vehicle operator while maintaining safety.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for monitoring vehicles traversing a dedicated roadway that includes an at-grade crossing. In some implementations, a system includes a central server, a gate system, and sensors. The gate system provides access to an at-grade crossing for vehicles. The sensors are positioned in a fixed location relative to a roadway, the roadway including the at-grade crossing. Each sensor can detect vehicles on the roadway. For each vehicle, each sensor can generate sensor data and observational data from the generated sensor data. Each sensor can determine a likelihood that the detected vehicle will approach the at-grade crossing by comparing the likelihood to a threshold. In response, each sensor can transmit data to the gate system that causes the gate system to allow the autonomous vehicle access to the at-grade crossing prior to the autonomous vehicle reaching the gate system.