Autonomous Vehicle Navigation via Constrained Minimization Cost Function
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Solution Overview
Problem
Current autonomous vehicle navigation systems lack the capability to navigate reliably and safely without human intervention, as they struggle to optimize routes considering multiple competing factors such as safety, efficiency, comfort, and environmental impact, and are prone to errors due to incomplete data processing and reaction delays.
Innovation Solution
The development of a system that uses physical models of vehicles and environments, combined with real-time sensor data, to calculate and update optimal navigation paths by constructing a cost function that weighs safety, comfort, fuel consumption, and time, allowing for incremental and iterative vehicle control adjustments to respond to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicle navigation systems use basic routing algorithms, then the system complexity is low, but the navigation reliability and safety are insufficient
Solution Approach 1:
The navigation system is divided into multiple independent modules: cost function construction module, physical model module, sensor data processing module, and trajectory optimization module. Each module handles specific aspects of navigation, allowing the system to achieve high reliability through modular design while managing complexity through functional separation.
Solution Approach 2:
The system pre-constructs physical models of vehicles and environments before actual navigation occurs. These models include vehicle dynamics characteristics, environmental constraints, and cost function parameters. By preparing these models in advance, the system ensures reliable navigation decisions can be made quickly during real-time operation without excessive computational complexity.
2Reliability
If the system considers multiple competing factors (safety, efficiency, comfort, environmental impact), then the navigation quality improves, but the data processing complexity increases
Solution Approach 1:
Multiple competing factors (safety, efficiency, comfort, environmental impact) are merged into a single unified cost function. This cost function integrates diverse parameters into one comprehensive metric that the trajectory optimization module can process, thereby improving navigation quality through multi-factor consideration while managing data processing complexity through unified formulation.
Solution Approach 2:
The system transforms qualitative navigation requirements (safety, comfort, efficiency) into quantifiable parameters that can be processed computationally. By defining specific parameter relationships and weightings in the cost function, the system enables systematic optimization across multiple dimensions without overwhelming processing complexity.
3Adaptability or versatility
If the system uses real-time sensor data and physical models, then the navigation adaptability improves, but the computational requirements increase
Solution Approach 1:
The system continuously receives real-time sensor data from the vehicle and environment, compares actual conditions with predictions from physical models, and adjusts the cost function and trajectory accordingly. This feedback mechanism enables high environmental adaptability while managing computational energy through iterative refinement rather than complete recalculation.
Solution Approach 2:
The navigation system dynamically updates physical models and cost function parameters based on changing environmental conditions and vehicle state. By making the system adaptive and dynamic rather than static, it achieves high environmental versatility while optimizing computational energy usage through incremental updates to models and parameters as conditions change.
4Manufacturing precision
If the system performs iterative control adjustments, then the navigation precision improves, but the response time increases
Solution Approach 1:
The system performs iterative control adjustments at strategically selected intervals and for specific critical parameters rather than continuously optimizing all aspects. This partial action approach maintains trajectory precision through focused iterative refinement while minimizing response time losses by avoiding unnecessary full-system re-optimization at every moment.
Data Source
AI summary
Disclosed is a system for automatically navigating a vehicle, which system comprises:a data storage containing one or more maps of one or more areas;electronic interfaces for: (i) receiving data relating to the one or more areas from external data sources; (ii) receiving parameters relating to motion of the vehicle from physical sensors; and (iii) receiving a location of the vehicle from a positioning device;a navigation processor comprising processing circuitry adapted to determine an initial optimal route for traversing the vehicle from a given point in the one or more areas to another point in the one or more areas by computationally resolving a constrained minimization problem.