Vehicle Sensor Discrepancy Detection via Path Curvature Comparison
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Solution Overview
Problem
As vehicle automation levels increase, there is a growing need for precise sensor systems to accurately detect vehicle position and path curvature, but discrepancies between sensors can lead to inaccurate data, potentially causing autonomous vehicles to lose control, necessitating a method to detect and correct such discrepancies.
Innovation Solution
The system employs multiple sensors to detect vehicle position and path curvature, with a controller comparing parameters to identify discrepancies, triggering a diagnostic mode that can disable faulty sensors, request backup control, communicate with remote devices, or realign sensors to maintain accurate data and ensure safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to detect vehicle position and path curvature, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system continuously compares path curvature parameters from multiple sensors (first sensor from vehicle position coordinates, second sensor from direct path detection) and uses this feedback to detect discrepancies. When a discrepancy exceeds a threshold, the system triggers diagnostic mode, creating a closed-loop feedback mechanism that maintains measurement precision through active monitoring and correction.
Solution Approach 2:
The sensor system is segmented into functionally distinct components: a first sensor (e.g., GPS/odometry) for detecting vehicle position and calculating path curvature, and a second sensor (e.g., camera/visual system) for directly detecting path curvature. This segmentation allows independent validation of each sensor's measurements and facilitates targeted diagnostics when discrepancies occur.
2Reliability
If sensor discrepancies are not detected, then device complexity remains low, but reliability deteriorates due to potential loss of control
Solution Approach 1:
The controller continuously monitors sensor data and compares path curvature parameters from multiple sources. This real-time feedback mechanism detects discrepancies between sensors, triggering diagnostic mode when reliability thresholds are compromised, thereby maintaining autonomous control safety through active monitoring.
Solution Approach 2:
The system performs preliminary diagnostic actions by continuously comparing sensor parameters before discrepancies lead to control failures. The controller is pre-configured with threshold values and diagnostic protocols, enabling it to proactively detect and respond to sensor failures before they compromise vehicle safety.
3Reliability
If automatic diagnostic mode is triggered, then reliability is improved by correcting sensor errors, but loss of time occurs due to diagnostic procedures
Solution Approach 1:
The system performs preliminary comparisons of sensor parameters continuously during normal operation, so when a discrepancy exceeds the threshold, the diagnostic mode is already primed and can execute immediately. The threshold-based trigger mechanism ensures diagnostics are initiated only when necessary, reducing unnecessary time loss while maintaining reliability.
Solution Approach 2:
When a discrepancy exceeds the threshold, the system rapidly transitions to diagnostic mode and executes corrective actions (such as disabling the faulty sensor or switching to backup control) without prolonged deliberation. This rushing through the diagnostic process minimizes time loss while ensuring reliability is restored.
Data Source
AI summary
A method of controlling a ground vehicle includes providing the vehicle with a first sensor configured to detect a vehicle position, a second sensor configured to detect a driven path of the vehicle, and a controller. The method also includes obtaining, via the first sensor, a plurality of vehicle position coordinates during a time interval of a drive cycle, and calculating, via the controller, a first path curvature parameter based on the plurality of vehicle position coordinates. The method additionally includes obtaining, via the second sensor, a second path curvature parameter based on the driven path during the time interval. The method further includes comparing, via the controller, the first path curvature parameter to the second path curvature parameter, and in response to a difference between the second path curvature and the first path curvature exceeding a threshold, automatically operating the controller according to a diagnostic mode.