Lane Detection Uncertainty Prediction via CNN Error Feedback
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Solution Overview
Problem
Current navigation systems for autonomous and semi-autonomous vehicles face challenges in accurately predicting lane detection uncertainty, leading to potential errors in vehicle navigation, especially in environments with missing, contradictory, or displaced lane markings.
Innovation Solution
A system utilizing a convolutional neural network (CNN) that analyzes input images from sensors like cameras or LiDAR devices to predict lane geometries and uncertainty, trained using per point association and error calculation, which generates a commanded navigation plot to guide the vehicle and account for uncertainty in lane detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image recognition programming is used to detect lane geometries from sensor inputs, then the system can identify lane markings and navigate, but the system introduces inherent prediction errors that reduce navigation reliability
Solution Approach 1:
The system implements feedback by calculating per-point errors between predicted lane points and ground truth lane points during training. The error prediction head network receives these error values as feedback signals to learn and minimize prediction errors, thereby improving reliability while maintaining productivity
Solution Approach 2:
The patent replaces traditional mechanical error measurement methods with a neural network-based error prediction system. The error prediction head network substitutes conventional validation approaches by learning error patterns directly from training data, enabling continuous error estimation during operation without additional computational overhead
2Productivity
If a convolutional neural network is trained to predict lane geometries, then the system can process sensor data efficiently, but the system requires complex training procedures including per point association and error calculation
Solution Approach 1:
The training process is segmented into distinct components: the tiles prediction deep network handles image processing and initial lane prediction, while the error prediction head network separately learns error patterns. This segmentation allows each component to be optimized independently, reducing overall training complexity while maintaining high processing speed
Solution Approach 2:
The system performs preliminary association between predicted lane points and ground truth points during the training phase. This preliminary action establishes the foundation for error calculation before the actual training occurs, simplifying the training process by pre-processing the data into a usable format for the error prediction head
3Measurement precision
If the system calculates per point errors between predicted and ground truth lanes, then the system can improve prediction accuracy, but the system increases computational complexity in the training process
Solution Approach 1:
The error calculation functionality is extracted from the main tiles prediction deep network and placed into a separate error prediction head network. This extraction allows error measurement to be performed independently with optimized algorithms, improving measurement precision while reducing the computational burden on the main prediction network during operation
Data Source
AI summary
A system for end to end prediction of lane detection uncertainty includes a sensor device of a host vehicle generating data related to a road surface and a navigation controller including a computerized processor operable to monitor an input image from the sensor device, utilize a convolutional neural network to analyze the input image and output a lane prediction and a lane uncertainty prediction, and generate a commanded navigation plot based upon the lane prediction and the lane uncertainty prediction. The convolutional neural network is initially trained using a per point association and error calculation, including associating a selected ground truth lane to a selected set of data points related to a predicted lane and then associating at least one point of the selected ground truth lane to a corresponding data point from the selected set of data points related to the predicted lane.