Vehicle Localization Control for Large Yaw, Pitch, and Roll Changes
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Solution Overview
Problem
Conventional vehicular control systems relying on GPS and inertial sensors face significant errors in predicting vehicle location due to assumptions of small angular disturbances, particularly in yaw, pitch, and roll, which are not valid in real-world scenarios like turning or navigating hills.
Innovation Solution
A vehicular control system that uses vehicle dynamics sensors to capture acceleration and angular velocity data, processes this information with an electronic control unit to determine sensitivity to changes in pitch, roll, and yaw, and predicts future locations using mathematical models derived from kinematics, allowing for more accurate long-term motion planning and fusion of localization methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional GPS and inertial sensors are used with small angular disturbance assumptions, then the system complexity remains low, but the localization accuracy deteriorates significantly in real-world scenarios
Solution Approach 1:
The patent changes the mathematical parameters used in motion prediction from small-angle approximations to exact trigonometric relationships. The system uses full kinematic models with sine and cosine functions to account for large angular disturbances in yaw, pitch, and roll, thereby maintaining high localization accuracy without requiring additional sensors or hardware complexity
Solution Approach 2:
The patent replaces the simplified mechanical assumption of small angular disturbances with a more sophisticated mathematical model based on exact kinematic equations. By substituting the mechanical approximation with precise trigonometric calculations, the system achieves higher accuracy while keeping the computational framework manageable through efficient algorithm design
2Measurement precision
If small angular disturbance assumptions are made for yaw, pitch, and roll, then the computational model remains simple, but the prediction accuracy deteriorates during vehicle maneuvers
Solution Approach 1:
The patent transforms the mathematical parameters from linearized small-angle approximations to exact trigonometric expressions. The state of motion prediction uses full sine and cosine functions to accurately represent large angular changes during maneuvers, while the computational complexity is managed through efficient implementation of these transcendental functions
Solution Approach 2:
The patent introduces dynamic adaptation by selecting different mathematical models based on the magnitude of angular disturbances. During normal operation with small disturbances, simpler models may be used, but during maneuvers with large angular changes, the system dynamically switches to the full kinematic model with trigonometric functions, optimizing both accuracy and computational efficiency
3Measurement precision
If GPS-only localization is used, then the system implementation remains simple, but the localization accuracy deteriorates in areas with poor satellite reception or during dynamic maneuvers
Solution Approach 1:
The patent merges GPS position data with inertial sensor data (accelerometers and gyroscopes) through sensor fusion algorithms. By combining the absolute position information from GPS with the relative motion information from inertial sensors, the system achieves continuous and accurate localization even when GPS signals are degraded or unavailable during dynamic maneuvers
Solution Approach 2:
The patent introduces an intermediary computational model that bridges GPS and inertial sensor data. This intermediate kinematic model processes inertial measurements to predict vehicle position and orientation, then reconciles these predictions with GPS observations, effectively mediating between the two sensor types to produce accurate localization results
Data Source
AI summary
A vehicular control system includes a vehicle dynamics sensor disposed at a vehicle and capturing vehicle dynamics data representative of a state of motion of the vehicle. The vehicular control system, when the vehicle is at a first location on a road the vehicle is traveling along, determines the state of motion of the vehicle via processing of vehicle dynamics data captured by the vehicle dynamics sensor. The vehicular control system, after the vehicle has traveled along the road from the first location to a second location, predicts a second location of the vehicle based on the state of motion of the vehicle at the first location. The vehicular control system determines a sensitivity of the state of motion of the vehicle to change and corrects the predicted second location of the vehicle based at least in part on a global positioning system of the vehicle.