Autonomous Fleet De-Icing Using Real-Time Road Condition Feedback
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Solution Overview
Problem
Existing road de-icing methods using autonomous vehicles face inefficiencies due to over- or under-application of de-icing materials, leading to environmental damage, vehicle deterioration, and increased financial burden, primarily caused by unreliable meteorological predictions and lack of real-time data integration.
Innovation Solution
A fleet of autonomous vehicles equipped with sensors and a mission control computing system that uses real-time sensor data, fleet data, and meteorological information to adaptively determine and adjust de-icing strategies, including the type, amount, and location of de-icing materials based on actual road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If de-icing material is applied based on unreliable meteorological predictions, then road safety may be improved, but environmental damage occurs and material waste increases
Solution Approach 1:
The system implements feedback by collecting real-time sensor data from autonomous vehicles about actual road conditions (ice presence, temperature, humidity) and using this information to adjust and optimize de-icing material application rates, replacing unreliable meteorological predictions with actual observed data
Solution Approach 2:
The autonomous vehicles perform self-service by autonomously collecting their own sensor data about road conditions and using this data to determine their own de-icing material application needs, eliminating the need for external meteorological predictions
2Reliability
If de-icing material is over-applied to ensure safety, then road safety is maintained, but vehicle deterioration accelerates and financial burden increases
Solution Approach 1:
The system dynamically changes the parameters of de-icing material application (amount, frequency, timing) based on real-time sensor data from autonomous vehicles, adjusting these parameters to match actual road conditions rather than using fixed over-application rates
3Measurement precision
If real-time sensor data and fleet data are integrated, then de-icing precision is improved, but system complexity increases
Solution Approach 1:
The system merges sensor data from multiple autonomous vehicles in a fleet into a centralized system, combining individual vehicle measurements into collective fleet data that improves overall measurement precision and enables optimized de-icing strategies across the entire fleet
Data Source
AI summary
An autonomous vehicle is provided. The autonomous vehicle includes one or more sensors and an autonomy computing system. The autonomy computing system includes at least one processor in communication with at least one memory device. The at least one processor is programmed to receive sensor data from the one or more sensors, and receive weather-related data from a mission control computing system. The weather-related data include fleet data from autonomous vehicles in a fleet. The fleet includes the autonomous vehicle. The at least one processor is further programmed to determine an icy condition is present based on the sensor data and the fleet data, and determine de-icing strategies based on the icy condition.