AI Vehicle Speed Control Based on Stopping Distance
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Solution Overview
Problem
Conventional vehicle speed management systems fail to account for dynamic factors such as geography, weather conditions, and vehicle state information, leading to potential safety risks by permitting vehicles to travel beyond or below restricted speeds.
Innovation Solution
A system utilizing onboard vision sensors and an AI neural network to determine a vehicle's stopping distance and speed limit based on image data, adjusting speed to prevent unwarranted movement and enhance safety by considering the range of vision and other factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems use predefined and static speed limits for different locations, then the systems can operate with simple rules, but the systems may not account for geography, weather conditions, and vehicle state information leading to safety risks
Solution Approach 1:
The patent transforms static speed limits into dynamic speed limits that automatically adjust based on real-time conditions. The system determines dynamic restricted speeds by considering geography, weather conditions, and vehicle state information, allowing speed limits to adapt continuously to changing environmental factors rather than remaining fixed.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring weather conditions, geographic data, and vehicle state information, then using this feedback to adjust speed limits dynamically. The automated safety systems receive ongoing data about environmental factors and modify speed restrictions accordingly to maintain safety while accounting for current conditions.
2Reliability
If systems permit dynamic restricted speed based on geography, weather conditions, and vehicle state information, then safety can be improved, but vehicles may travel in excess of or below restricted speeds posing safety risks
Solution Approach 1:
The system enables vehicles to self-regulate their speeds by providing them with dynamically determined restricted speeds based on their own state information and environmental conditions. Each vehicle independently receives and adheres to its own customized speed limits calculated considering its specific characteristics and current operating environment.
Solution Approach 2:
The system changes the parameters of speed limits from fixed values to variable parameters that depend on multiple factors including vehicle mass, length, height, consist, weather conditions, and geographic characteristics. This allows speed restrictions to be customized for each vehicle based on its specific parameters and current conditions.
3Reliability
If automated safety systems communicate maximum restricted speed in response to static or dynamic conditions, then safety can be enhanced, but the systems may require stopping vehicles when events occur that do not necessitate stopping
Solution Approach 1:
The system applies partial action by communicating restricted speeds rather than requiring complete stops for all events. Instead of stopping vehicles for every detected condition, the system adjusts speed limits to partially restrict movement based on the severity and nature of the event, allowing vehicles to continue moving at reduced speeds when full stops are unnecessary.
Solution Approach 2:
The system dynamically adjusts speed restrictions based on the specific event or condition detected. Rather than applying uniform stop commands, the system modifies speed limits in real-time according to the nature of the event, allowing flexible response that maintains safety while avoiding unnecessary complete stops.
Data Source
AI summary
A method is provided that may include obtaining image data from vision sensors disposed onboard a vehicle. The method may include determining a stopping distance of the vehicle based at least in part on the image data using an artificial intelligence (AI) neural network having artificial neurons arranged in layers and connected with each other by connections. A moving speed and a speed limit of the vehicle may be determined using the AI neural network. The method may control movement of the vehicle using the AI neural network by enforcing movement authorities preventing unwarranted movement of the vehicle based on a difference between the moving speed and the speed limit. The method may include receiving feedback regarding the stopping distance and the speed limit calculated by the artificial neurons and training the AI neural network by changing connections between the artificial neurons based on the feedback received.