Vehicle Position Precision Assessment Using Dual Location Estimates
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Solution Overview
Problem
Autonomous control systems in vehicles face inaccuracies in estimating the current location due to road feature conditions, movement errors, and lateral sliding, which can compromise safe navigation.
Innovation Solution
A positional precision assessment device that estimates a first location based on road features and a second location based on previous movement, using a prediction filter to calculate the current location and assess its precision, with an assessment unit determining the state of precision and notifying the driver when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the autonomous control system estimates the current location based on road features and movement data, then the vehicle can navigate autonomously, but the location accuracy deteriorates due to road feature conditions, movement errors, and lateral sliding
Solution Approach 1:
The system performs preliminary location estimation using multiple methods (road feature recognition and movement data integration) before final navigation decisions are made. This allows the system to have multiple candidate location estimates ready, which can be cross-validated to improve accuracy while maintaining autonomous operation
Solution Approach 2:
The system introduces an intermediary assessment mechanism that evaluates the precision of location estimates before they are used for navigation. This intermediary layer checks whether the estimated location meets required precision thresholds, and if not, triggers appropriate responses such as requesting manual input or adjusting navigation parameters
2Measurement precision
If the system uses multiple estimation methods to improve location accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The location estimation system is segmented into distinct functional modules: road feature recognition module, movement data integration module, and precision assessment module. Each module handles a specific aspect of location estimation, making the overall complex system manageable and maintainable while achieving high accuracy through coordinated operation of these segmented components
Solution Approach 2:
The precision assessment mechanism serves multiple functions: it evaluates location accuracy, determines whether manual input is needed, and guides navigation decisions. This multi-functional approach reduces the need for separate systems for each function, thereby managing complexity while maintaining high measurement precision
3Reliability
If the system continuously monitors location precision to ensure safety, then the reliability improves, but the use of energy increases due to continuous calculation and assessment
Solution Approach 1:
Instead of continuous monitoring, the system performs location precision assessment at periodic intervals and at key decision points during navigation. This periodic action maintains navigation safety by regularly verifying location accuracy while significantly reducing energy consumption compared to continuous real-time monitoring
4Measurement precision
If the system requests driver intervention when precision is poor, then the measurement precision improves through manual input, but the productivity decreases due to reduced autonomous operation time
Solution Approach 1:
The system implements a feedback mechanism where the precision assessment results directly influence operational mode decisions. When precision is sufficient, the system continues autonomous operation; when precision falls below thresholds, the system feedbacks the need for manual intervention. This feedback loop ensures high productivity by maximizing autonomous operation time while maintaining safety through selective driver engagement
Data Source
AI summary
The positional precision assessment device has a processor configured to estimate a first location of a moving object at a first time based on an image representing road features and positional information, estimate a second location at the first time based on a location of the moving object at the second time and an amount of movement and change in direction from the second time to the first time, input the first and second location into a filter and calculate a current location at the first time, and determine a state of precision of the current location based on a difference between the current location of the moving object and a location estimated based on a location of the moving object at a time prior to the first time and an amount of movement and change in direction from the prior time to the first time.