Trailer State Estimation Using Coupled Kinematic Filtering
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Solution Overview
Problem
Existing vehicle-trailer systems face challenges in accurately predicting the position and heading of vehicles and trailers during low-speed maneuvers due to the use of high-fidelity models that consume excessive computational resources and noisy, low-cost sensors, leading to error accumulation and performance degradation.
Innovation Solution
A driver assistance system utilizing multiple vehicle sensors to predict and correct the state of the vehicle and trailer using a coupled vehicle-trailer state estimator, which employs kinematic models and adaptive filtering techniques to improve accuracy and adapt to varying sensor configurations, while addressing sensor dead zones and noise issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity models are used to predict vehicle and trailer state, then prediction accuracy is improved, but computational resource consumption increases excessively
Solution Approach 1:
The system changes the parameters of the prediction model by using a coupled vehicle-trailer state estimator that operates at reduced computational complexity compared to high-fidelity models, while maintaining adequate prediction accuracy for low-speed maneuvers
Solution Approach 2:
The system dynamically adapts the prediction approach by using adaptive filtering techniques that adjust to varying sensor configurations and maneuver conditions, optimizing the balance between accuracy and computational cost in real-time
2Device complexity
If low-cost sensors are used in the system, then device complexity and cost are reduced, but measurement noise increases leading to error accumulation
Solution Approach 1:
The system implements feedback through the coupled state estimator that continuously processes sensor measurements and corrects predicted states, using feedback loops to compensate for noise and error accumulation from low-cost sensors
Solution Approach 2:
The system uses a composite estimation approach that combines multiple sensor inputs and prediction models to create a more reliable state estimate than any single sensor could provide, effectively filtering noise through the integrated estimation process
3Measurement precision
If adaptive filtering techniques are applied to correct predicted states, then measurement accuracy is improved, but processing time increases
Solution Approach 1:
The system optimizes processing efficiency by tuning the filtering parameters and sensor measurement frequencies to achieve the necessary accuracy with minimal processing time, adapting to the specific requirements of low-speed trailering maneuvers
Data Source
AI summary
A vehicular trailer assist system includes a camera disposed at a rear portion of a vehicle, a plurality of sensors, and an electronic control unit (ECU) having a processor for processing image data captured by the camera and sensor data captured by the plurality of sensors. The ECU, responsive to processing of image data captured by the camera, determines a trailer angle of the trailer relative to the vehicle. The ECU, responsive to processing of sensor data captured by the plurality of sensors, predicts a yaw rate of the vehicle and a trailer angle of the trailer relative to the vehicle at a future time. The ECU, responsive to predicting the yaw rate, determines a corrected vehicle yaw rate based on processing of captured image data and captured sensor data, and determines a corrected trailer angle, and determines a vehicle heading and position and a trailer heading and position.