Multi-Sensor Object Segmentation for Hazard Detection in Adverse Driving
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Solution Overview
Problem
Deep neural networks face challenges in accurately detecting objects in dynamic environments due to labeling ambiguity, out-of-distribution problems, and difficulties in handling adverse conditions such as hazy or blurry images, which can lead to unpredictable results and missed detections of small or far-away objects.
Innovation Solution
A method that combines sensor data from image, radar, and ultrasound sensors using a convolutional neural network to perform class-agnostic object detection, segmenting images without labeling and estimating hazard probabilities for objects, enabling real-time vehicle operation by controlling powertrain, brakes, and steering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks are trained using traditional labeled data approaches, then the system can perform basic object detection, but it fails to accurately detect objects in adverse conditions and produces unpredictable results due to labeling ambiguity and out-of-distribution problems
Solution Approach 1:
The patent segments the detection task by separating object detection from semantic classification. The system performs class-agnostic object detection to identify potential hazards without requiring labeled training data for specific object classes, thereby improving reliability in adverse conditions while maintaining adaptability to unseen objects.
Solution Approach 2:
The patent introduces an intermediary segmentation map that bridges sensor data and hazard detection. The segmentation map provides structured spatial information about objects in the scene, enabling the system to detect hazards reliably without being constrained by labeling ambiguity or out-of-distribution problems.
2Measurement precision
If the system processes detailed labeled data for accurate object identification, then detection precision improves, but processing time increases and real-time performance deteriorates
Solution Approach 1:
The patent extracts only the essential spatial and structural information needed for hazard detection from sensor data, rather than processing complete labeled object information. By taking out only the necessary segmentation features, the system achieves high detection precision while maintaining real-time processing speed.
Solution Approach 2:
The patent applies partial action by performing detection only on segmented regions that contain potential hazards, rather than analyzing every pixel or object in the scene. This selective processing maintains precision for critical detections while improving overall processing throughput for real-time operation.
3Reliability
If the system uses multiple sensor types for comprehensive environmental perception, then detection coverage improves, but system complexity increases
Solution Approach 1:
The patent merges data from multiple sensor types (cameras, LIDAR, radar) into a unified segmentation framework. By combining sensor inputs at the segmentation level rather than processing each sensor separately, the system achieves comprehensive environmental perception while reducing the complexity of sensor integration and data fusion.
Data Source
AI summary
First sensor data and second sensor data can be combined by inputting the first sensor data and second sensor data to a deep neural network. A segmentation map from the combined sensor data that includes labeled segments, wherein the labeled segments include (a) pixels corresponding to objects in the combined sensor data, (b) hazard probabilities for respective labeled segments included in the segmentation map can be determined in the deep neural network based on the combined first sensor data and the second sensor data. The segmentation map and the hazard probabilities can be output.