Clear Path Probability Map for Vehicle Detection
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Solution Overview
Problem
Current vision-imaging systems for vehicle path detection are slow due to intense data processing and struggle to distinguish clear path features from non-clear path features, affecting the efficiency of autonomous or semi-autonomous driving systems.
Innovation Solution
A method that generates a clear path probability map by capturing images at various time steps, using a processing module to create a weight-matching map that updates probability values based on feature matching between time steps, and applies these weights to determine the likelihood of features being part of the clear path, thereby improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional vision-imaging systems process all extracted features to identify clear path features, then detection accuracy may be maintained, but processing speed decreases due to intense data processing
Solution Approach 1:
The system performs preliminary action by generating a correspondence mapping between current and previous time step frames before full feature analysis. This mapping pre-identifies which features are likely to be clear path features based on temporal coherence, allowing the system to prioritize processing of high-probability features and skip or reduce processing of low-probability features, thereby improving detection speed without sacrificing accuracy
Solution Approach 2:
The system applies local quality by treating different features differently based on their individual probability values. Features with high probability of being clear path features receive full processing attention, while features with low probability receive minimal or no processing. This selective processing approach reduces overall computational load while maintaining accurate identification of actual clear path features
2Device complexity
If the system processes features from each time step independently without temporal context, then processing complexity is reduced, but the ability to distinguish clear path features from non-clear path features deteriorates
Solution Approach 1:
The system implements feedback by using the clear path probability map from the previous time step frame to inform processing of the current time step frame. The correspondence mapping transfers temporal context from previous frames, providing feedback that helps distinguish clear path features from non-clear path features. This feedback mechanism improves feature distinction accuracy while maintaining manageable processing complexity through efficient probabilistic updates
Solution Approach 2:
The correspondence mapping acts as an intermediary between temporal frames, bridging the gap between independent time step analyses. This intermediary structure enables temporal context to be incorporated without requiring full re-processing of all features, thus improving feature distinction accuracy while keeping processing complexity at acceptable levels through selective information transfer
Data Source
AI summary
A method of detecting a clear path of travel. Input images are captured at various time step frames. Clear path probability maps of a current and previous time step frames are generated. A corresponding clear path probability map is generated for the current time step frame derived as a function of the clear path probability map of the previous time step frame and of a corresponding mapping that coherently links the previous time step frame to the current time step frame. A weight-matching map is generated. The probability values of the current time step frame are updated as a function of the corresponding probability map. A current frame probability decision map is generated based on updated probability values of the current time step frame. The clear path in the image of the current time step is identified based on the current frame probability decision map.