Autonomous Vehicle Location Estimation Using V2V Data
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Solution Overview
Problem
Autonomous driving vehicles face challenges in maintaining safe navigation when sensor faults occur, as the abnormal operation of vehicle sensors is not easily identifiable by pedestrians or drivers, leading to unreliable location estimation.
Innovation Solution
A method and system that calculate and select between internal and external vehicle locations using data from sensors like cameras, radars, lidars, and map information, with external location data obtained from surrounding vehicles, to ensure safe autonomous driving by comparing errors with a threshold value and controlling the vehicle to move to a safe zone if errors exceed the threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous driving vehicles rely solely on internal sensors (camera, radar, lidar) for location estimation, then the system can operate independently, but the location estimation becomes unreliable when sensor faults occur
Solution Approach 1:
The patent introduces other vehicles as intermediary sources of location information. When internal sensors fail, the autonomous vehicle receives location data from surrounding vehicles through V2V communication, serving as a mediator to maintain reliable location estimation without requiring complex backup sensor systems
Solution Approach 2:
The location determination system is designed to perform multiple functions: it can use internal sensor data under normal conditions, switch to external vehicle data when sensors fail, and automatically compare multiple data sources. This multi-functionality increases reliability without proportionally increasing system complexity
2Reliability
If the vehicle uses multiple location sources (internal and external) and compares errors with threshold values, then location reliability increases, but the processing complexity and time increase
Solution Approach 1:
The system performs error comparison between internal and external location data selectively rather than continuously. It activates full comparison processing only when sensor faults are detected or location accuracy is questionable, reducing time loss during normal operation while maintaining high reliability when needed
3Productivity
If the vehicle switches to external location data from other vehicles when internal sensors fail, then autonomous driving can continue, but the vehicle becomes dependent on surrounding vehicles
Solution Approach 1:
The system dynamically adjusts its data source selection based on real-time sensor health status. It maintains operational independence by having configurable fallback options: first attempting to use internal sensors, then switching to external vehicle data when sensors fail, and potentially moving to a safe zone if neither source is available. This dynamic adaptability ensures driving continuity while preserving operational flexibility
Data Source
AI summary
The present disclosure relates to a vehicle and a method of controlling the same, for keeping safe autonomous travel by calculating and using an estimated location on the basis of another vehicle information in case of occurrence of an error in estimating the location of a vehicle due to a sensor fault during autonomous driving. The method includes steps of: obtaining internal location data from an inside source of the vehicle and calculating an internal absolute location of the vehicle based on the internal location data for determining a location of the vehicle; obtaining external location data from an outside source of the vehicle and calculating an external absolute location of the vehicle based on the external location data for determining a location of the vehicle; and selecting at least one of the internal absolute location and the external absolute location as a current location of the vehicle.