Lane Marking Misdetection Root Cause Isolation in Automated Driving
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Solution Overview
Problem
Conventional vehicle systems, such as lane departure warning systems, face challenges in accurately detecting lane markings, especially in scenarios like heavy rain, snow, construction, obstacles, and sensor failures, leading to misdetected lane markings which can incorrectly assume the absence of lanes and force drivers to take control, causing inconvenience.
Innovation Solution
A system comprising multiple sensors and a controller that executes parallel procedures to detect and isolate the root cause of lane marking misdetection, using image processing, machine learning models, and sensor comparisons to determine the presence of lane markings and mitigate errors by switching sensors, informing drivers, or scheduling maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional lane departure warning systems use single sensor-based lane marking detection, then the system structure remains simple, but the reliability deteriorates due to sensor failures and misdetection in challenging conditions
Solution Approach 1:
The patent combines multiple sensors (cameras, radars, Lidar) to form an integrated detection system. The controller receives and processes data from all sensors simultaneously, using their complementary strengths to overcome individual sensor limitations and achieve more reliable lane marking detection under various environmental conditions.
Solution Approach 2:
The detection system is designed to perform multiple functions: detecting lane markings, identifying environmental conditions (rain, snow, construction, obstacles), and determining sensor health status. This multi-functional approach allows the system to adapt to diverse scenarios and maintain reliability across different operating conditions.
2Measurement precision
If the system executes multiple parallel procedures to detect misdetection causes, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The system performs preliminary data processing and validation before full analysis. The controller first checks for obvious issues (such as comparing sensor data consistency, checking environmental conditions) and only executes comprehensive parallel procedures when necessary, reducing unnecessary processing time while maintaining accurate cause identification.
Solution Approach 2:
The detection system divides the analysis into separate modular procedures: one for detecting sensor faults, another for environmental conditions, another for lane marking presence, and so on. These procedures execute in parallel, allowing independent processing of different cause categories simultaneously, which improves efficiency while maintaining comprehensive analysis accuracy.
3Reliability
If the system switches to alternative sensors upon misdetection, then the reliability improves, but the device complexity increases
Solution Approach 1:
The controller acts as an intermediary that manages sensor switching automatically. When misdetection is identified, the controller selects appropriate alternative sensors based on pre-established criteria and environmental context, handling the complexity of sensor management internally while presenting a simplified interface for the overall system operation.
Data Source
AI summary
A system for a vehicle includes a plurality of sensors onboard the vehicle and a controller. A first sensor of the plurality of sensors is configured to detect lane markings on a roadway. The controller is configured to store data from the plurality of sensors. In response to receiving an indication indicating a misdetection of lane markings on the roadway based on data received from the first sensor, the controller is configured to execute in parallel a plurality of procedures configured to detect a plurality of causes for the misdetection of lane markings, respectively, based on the stored data; isolate one of the causes as a root cause for the misdetection of lane markings; and provide a response for mitigating the misdetection of lane markings on the roadway based on the root cause for the misdetection of lane markings.