Vehicle Axle Weight Sensing for Autonomous Braking and Steering
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Solution Overview
Problem
Conventional systems for determining vehicle weight distribution are inaccurate, relying on engine torque, brake torque, and inertial measurement units, which do not effectively account for load variations, impacting autonomous driving operations.
Innovation Solution
The use of wireless weight sensors to measure pressure and weight on vehicle axles and tire pressure monitoring system (TPMS) sensors to provide accurate weight distribution data, with error detection and removal processes to filter out erroneous values, enabling precise control of driving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use engine torque, brake torque, and inertial measurement units to determine weight distribution, then the system complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The patent divides the weight measurement function into multiple independent sensor units distributed at different locations (axle-mounted sensors and TPMS sensors). Each sensor independently measures local weight or pressure data, which are then aggregated to determine overall weight distribution. This segmentation approach improves measurement accuracy while keeping individual sensor units simple.
Solution Approach 2:
The patent combines data from two different sensor systems (axle-mounted weight sensors and TPMS pressure sensors) to determine weight distribution. By merging multiple measurement sources and cross-validating their readings, the system achieves higher measurement precision without requiring any single complex sensor.
2Measurement precision
If multiple sensor sets are used to improve weight distribution measurement, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system implements error detection and removal operations that use feedback mechanisms to identify and correct erroneous sensor readings. By continuously monitoring sensor data against expected ranges and statistical criteria, the system maintains high measurement precision while managing the complexity of multiple sensors through intelligent data validation.
Solution Approach 2:
The patent dynamically selects which sensor values to use based on statistical criteria such as standard deviation from average readings. This parameter-based selection approach allows the system to adaptively manage multiple sensor inputs, maintaining measurement precision while simplifying data processing by filtering out outliers.
3Reliability
If error detection and removal operations are performed on sensor values, then the reliability of weight distribution data improves, but the processing time increases
Solution Approach 1:
The system performs error detection and removal operations selectively rather than on all sensor data uniformly. By applying statistical criteria only when necessary and using pre-determined value ranges, the system achieves high data reliability while minimizing processing time through targeted rather than exhaustive validation.
Data Source
AI summary
Techniques are described for determining weight distribution of a vehicle. A method of performing autonomous driving operation includes determining a vehicle weight distribution that values for each axle of the vehicle that describe weight or pressure applied on a respective axle. The values of the vehicle weight distribution are determined by removing at least one value that is outside a range of pre-determined values from a set of sensor values. The method further includes determining a driving-related operation of the vehicle weight distribution. For example, the driving-related operation may include determining a braking amount for each axle and/or determining a maximum steering angle to operate the vehicle. The method further includes controlling one or more subsystems in the vehicle via an instruction related to the driving-related operation. For example, transmitting the instruction to the one or more subsystems causes the vehicle to perform the driving-related operation.