Vehicle Reference Speed Estimation Under High Wheel Slip
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Solution Overview
Problem
Existing methods for estimating the reference speed of a vehicle, particularly in borderline situations with high slip ratios, provide inaccurate information due to low friction surfaces, which affects the performance of anti-lock braking systems (ABS) and traction control systems (TCS) in electrically drivable land vehicles.
Innovation Solution
A method that determines four speed values using instantaneous wheel speed, longitudinal acceleration, GPS data, and camera data, which are then combined and weighted to estimate the true ground speed of a vehicle, utilizing artificial neural networks to compensate for deviations caused by driving conditions and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If reference speed is determined from CAN bus vehicle data, then the system is simple to operate, but measurement precision deteriorates in borderline situations with high slip ratios
Solution Approach 1:
The patent combines multiple speed determination methods (CAN bus data, GPS data, inertial sensor data, and wheel speed sensor data) into a unified reference speed estimation system. This merging allows the system to maintain simplicity of operation while achieving high measurement precision by fusing complementary information from different sources, especially under borderline driving conditions where single-source methods fail.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between raw sensor data and the final reference speed value. This intermediary layer processes and fuses data from multiple sources (CAN bus, GPS, inertial sensors, wheel speed sensors) to produce an accurate reference speed estimation, resolving the contradiction between operational simplicity and measurement precision.
2Measurement precision
If multiple speed values from different sources are combined, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent implements a universal reference speed determination system that can process multiple types of input data (CAN bus vehicle data, GPS data, inertial sensor data, wheel speed sensor data) through a single multi-functional processing architecture. This universal approach improves measurement precision while managing device complexity by using one integrated system rather than separate specialized systems for each data source.
Solution Approach 2:
The patent dynamically changes processing parameters based on driving conditions and data quality. The system adjusts the weighting and fusion strategy for different speed values depending on the current operating state (normal driving vs. borderline situations), which improves measurement precision without requiring permanently complex device architecture for all possible scenarios.
3Measurement precision
If artificial neural networks are used for speed estimation, then measurement precision improves in borderline situations, but use of energy increases
Solution Approach 1:
The patent implements a dynamic processing architecture that adapts computational intensity based on driving conditions. During normal driving, simpler methods are used to conserve energy. During borderline situations with high slip ratios, the system dynamically activates more computationally intensive neural network processing to improve measurement precision, thus optimizing the trade-off between energy consumption and accuracy.
Solution Approach 2:
The patent changes processing parameters dynamically based on detected driving conditions. When borderline situations are detected (high slip ratios), the system increases computational resources allocated to neural network processing. During normal conditions, processing parameters are reduced to minimize energy consumption, resolving the contradiction between measurement precision and energy use.
Data Source
AI summary
The invention relates to a method (1) for estimating a true ground speed (VRef) of an electrically drivable land vehicle by method of the vehicle's own evaluation electronics. In order to improve the estimation of the true speed (VRef), in particular in limit situations with restricted vehicle handling, a first speed value (V1) is determined taking into account an instantaneous wheel speed of at least one vehicle wheel of the land vehicle, a second speed value (V2) is determined taking into account a longitudinal acceleration of the land vehicle, a third speed value (V3) is determined taking into account received GPS data and a fourth speed value (V4) is determined taking into account camera data from a camera of the land vehicle, the true speed (VRef) being estimated taking into account these speed values (V1 to V4).