Vehicle Position Estimation Using Road Surface Roughness
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Solution Overview
Problem
Existing vehicle position estimation methods, particularly when there is little road surface unevenness in the lateral direction, suffer from low accuracy due to insufficient data for lateral position estimation.
Innovation Solution
A vehicle position estimation method that acquires time-series data of vertical motion parameters, compares them with reference data from a parameter map, and uses recognition sensors to enhance lateral position estimation by replacing estimated positions when road surface roughness is low, ensuring accurate positioning even in low-uneven road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle position estimation is performed using only vertical motion parameter comparison with a parameter map, then the system remains simple, but the lateral position estimation accuracy deteriorates when road surface unevenness is minimal
Solution Approach 1:
The patent introduces recognition sensors (cameras, LIDAR, radar) as intermediary devices to detect road surface features and lateral position. These sensors mediate between the vehicle's motion parameters and the parameter map, providing additional lateral position information that compensates for insufficient vertical motion data on smooth roads, thereby improving lateral position estimation accuracy without fundamentally redesigning the entire system
Solution Approach 2:
The patent merges two different estimation approaches: vertical motion parameter comparison (from parameter map) and recognition sensor-based lateral position detection. By combining these complementary methods, the system achieves accurate lateral position estimation even when one method alone would be insufficient, resolving the contradiction between measurement precision and device complexity
2Measurement precision
If recognition sensors are used to recognize lateral position when road surface roughness is low, then lateral position accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent implements dynamic sensor selection and data fusion based on road surface conditions. When road surface roughness is detected to be low (smooth roads), the system activates recognition sensors to compensate for insufficient vertical motion data. This dynamic adaptation allows the system to maintain high lateral position accuracy only when necessary, avoiding the permanent complexity of always-active sensor systems
Solution Approach 2:
The patent changes the operational parameters of the estimation system based on detected road surface roughness. When roughness is low, the system switches to using recognition sensor data for lateral position estimation. This parameter-based switching allows the system to optimize performance for different road conditions without requiring complex hardware for all possible scenarios
3Measurement precision
If vertical motion parameter data is filtered to improve estimation accuracy, then measurement precision improves, but response time increases
Solution Approach 1:
The patent applies filtering selectively and partially - only to the extent necessary to achieve acceptable accuracy. Rather than applying heavy, time-consuming filtering to all data, the system applies moderate filtering that provides sufficient accuracy improvement while minimizing processing time penalty, accepting a balance point rather than maximum possible filtering
Data Source
AI summary
A vehicle position estimation method includes: acquiring time-series data of a parameter related to a vertical motion of a wheel while the vehicle is traveling; acquiring the parameter around the vehicle, as a reference parameter, from a parameter map indicating a correspondence relationship between the parameter and a position; estimating a vehicle position based on a comparison between the time-series data of the parameter and time-series data of the reference parameter. Meanwhile, road surface roughness around the vehicle in a lateral direction and a lateral position of the vehicle in a road are recognized by using a recognition sensor installed on the vehicle. When the road surface roughness is less than a threshold, a lateral position component of the estimated vehicle position is replaced with the lateral position recognized by using the recognition sensor.