Vehicle Path Optimization for Lower Emissions and Fast Validation
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Solution Overview
Problem
Existing systems struggle to efficiently mitigate greenhouse gas emissions, particularly in high-density zones, due to complex relationships between environmental variables and the need for computationally efficient predictive data analysis to generate optimized vehicle operation plans without affecting other performance metrics.
Innovation Solution
A machine learning optimization model is used to determine emissions impact-optimized paths based on historical emissions data and environmental data, evaluated by validation engines to generate validated emissions impact-optimized paths for vehicle operations, considering factors like safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If complex environmental variables and historical emissions data are analyzed to generate optimized vehicle paths, then emissions impact mitigation is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing environmental variables and historical emissions data to create optimized vehicle paths before actual vehicle operations. The machine learning model is trained in advance on historical data, and optimized paths are generated beforehand for various route scenarios, enabling quick deployment during actual operations without real-time computational burden.
Solution Approach 2:
The system creates simplified copies of complex environmental and operational data through machine learning models. These models capture the essential relationships between environmental variables and emissions without requiring full complexity of the original data during optimization. The models replicate the behavior of complex systems in a computationally efficient manner.
2Object-affected harmful factors
If real-time optimized vehicle operation plans are generated to minimize emissions, then emissions impact is reduced, but processing speed and real-time performance may be affected
Solution Approach 1:
The system generates optimized vehicle operation plans in advance based on historical emissions data and environmental conditions. By pre-computing optimized paths and storing them for future reference, the system enables rapid deployment during actual operations without requiring time-consuming real-time calculations, thus maintaining both low emissions and high processing speed.
Solution Approach 2:
The patent replaces complex real-time mechanical computational systems with machine learning-based predictive models. These models have been trained offline to capture the relationships between environmental variables and emissions, allowing the system to make rapid predictions without performing heavy real-time computations, thereby achieving both accuracy and speed.
3Reliability
If multiple validation engines are used to evaluate optimized paths, then reliability and safety are improved, but system complexity and processing time increase
Solution Approach 1:
The validation system is segmented into multiple independent validation engines, each responsible for evaluating specific aspects of optimized paths such as safety, emissions compliance, and operational feasibility. This modular segmentation allows each engine to focus on particular validation tasks, improving overall reliability while maintaining manageable complexity through clear separation of concerns.
Data Source
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AI summary
Embodiments of the present disclosure provide techniques for generating emissions impact-optimized optimized paths. The techniques may include identifying input data set for a target vehicle operation, the input data set; determining using a machine learning optimization model, an emissions impact-optimized path based on the input data set and historical emissions impact data associated with a plurality of historical vehicle operations; evaluating the emissions impact-optimized path, based on one or more validation engines, to generate an evaluation output; and determining a validated emissions impact-optimized path for the target vehicle operation based on the emissions impact-optimized path and the evaluation output.