Vehicle Position Detection with Dynamic GNSS Jump Thresholds
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Solution Overview
Problem
Existing autonomous driving systems face instability due to 'position jumps' in satellite positioning, which are not adequately addressed by current techniques that fail to consider vehicle characteristics and require costly and labor-intensive testing, and struggle with simulating satellite reception and gyro sensor reactions in autonomous driving simulators.
Innovation Solution
A vehicle position detection device that calculates satellite and autonomous navigation positions, stores parameter sets with speed-specific thresholds for each vehicle characteristic, and selects the appropriate threshold to stabilize vehicle behavior by synchronizing GNSS and gyro sensor signals with a simulator environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single threshold is used for position jump determination regardless of vehicle characteristics, then the system is simple to operate, but light vehicles experience sudden steering changes and unstable behavior due to excessive threshold values
Solution Approach 1:
The threshold for position jump determination is made dynamic by adjusting it according to vehicle characteristics (weight class). The system automatically selects appropriate threshold values from stored parameters based on the detected vehicle type, allowing the threshold to adapt to different vehicle dynamics rather than using a fixed value for all vehicles.
Solution Approach 2:
The system changes the determination threshold parameter based on vehicle characteristics. Different threshold values are stored in the threshold storage unit corresponding to different vehicle types (light, medium, heavy vehicles), and the appropriate parameter is selected during operation to match the vehicle's weight class.
2Reliability
If thresholds are adjusted based on vehicle characteristics, then vehicle behavior stability improves, but the system complexity increases due to multiple parameter sets
Solution Approach 1:
The system segments the threshold parameters into distinct groups based on vehicle characteristics (light vehicle threshold, medium vehicle threshold, heavy vehicle threshold). This segmentation allows each vehicle type to have optimized parameters while keeping the overall system manageable through clear categorization.
Solution Approach 2:
The system performs self-service by automatically detecting vehicle characteristics and selecting the appropriate threshold parameter set without requiring manual configuration. The vehicle characteristic detection unit and parameter selection unit work together to automatically match the vehicle type with the correct threshold values, eliminating the need for complex manual parameter management.
3Measurement precision
If traveling tests are conducted with prototype self-driving vehicles to create position jump thresholds, then the thresholds are optimized for actual vehicle behavior, but the cost and man-hours required are greatly increased
Solution Approach 1:
Instead of conducting extensive physical traveling tests with prototype vehicles, the system uses a simulation environment that copies and reproduces the necessary test conditions. The simulation creates virtual scenarios including position jumps and various vehicle behaviors, allowing threshold parameters to be optimized without requiring actual physical testing.
Solution Approach 2:
The system performs preliminary threshold optimization through simulation before actual vehicle deployment. By conducting virtual tests and determining appropriate thresholds in advance using the simulation environment, the system prepares optimized parameters beforehand, eliminating the need for time-consuming field testing with prototype vehicles.
4Loss of time
If an autonomous driving simulator is used instead of actual vehicles, then testing costs and man-hours are reduced, but it becomes difficult to transmit satellite reception signals and cause gyro sensor reactions
Solution Approach 1:
The system introduces an intermediary interface layer between the simulation environment and the vehicle position detection device. This intermediary translates virtual satellite signals and gyro sensor inputs from the simulation into formats that the actual detection device can process, allowing the simulator to effectively communicate with the physical system without requiring complex physical modifications.
Data Source
AI summary
It is possible to reduce inadvertent vehicle behavior during autonomous driving. A vehicle position detection device includes: a satellite positioning position calculation unit 15 that receives a satellite positioning position signal of a vehicle and calculates a satellite positioning position of the vehicle; an autonomous navigation position calculation unit 16 that receives an autonomous navigation position signal of the vehicle and calculates an autonomous navigation position of the vehicle; a distance calculation unit 17 that calculates a positional difference between the satellite positioning position and the autonomous navigation position; a recording unit that stores a plurality of parameter sets in which a plurality of thresholds for allowing a positional difference for each speed of the vehicle are set per vehicle characteristic; a selection unit 14 that selects a parameter set according to the vehicle characteristic from a plurality of parameter sets 11 to 13 stored in the recording unit; and a determination unit 18 that outputs the satellite positioning position when the positional difference is within the threshold, and outputs the autonomous navigation position when the positional difference is out of the threshold.