Vehicle Captioning System Using Sensor Fusion for Safety Events
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Solution Overview
Problem
Current video captioning techniques are inefficient in generating human-understandable captions for safety-critical events from dashcam videos, consuming excessive computing resources and failing to prevent traffic-related deaths and injuries.
Innovation Solution
A captioning system that uses an encoder-decoder architecture to process video and sensor data from vehicles, generating captions through a convolutional neural network model and selecting appropriate decoder models to conserve resources and improve caption accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current video captioning techniques are used to generate captions for safety-critical events, then captions can be generated, but excessive computing resources are consumed and caption accuracy is insufficient
Solution Approach 1:
The system segments video processing into two distinct paths: a fast path for normal driving events using lightweight captioning, and a slow path for safety-critical events using comprehensive analysis with sensor data fusion. This segmentation allows resource-efficient processing while maintaining high accuracy for critical events.
Solution Approach 2:
The system dynamically changes processing parameters based on event severity. For safety-critical events detected through sensor data anomalies (acceleration, GPS, gyroscope), the system activates full captioning with sensor fusion. For normal events, it uses streamlined processing, thereby optimizing computing resource usage across different operating conditions.
2Measurement precision
If manual labeling of each video frame is performed, then accurate captions can be obtained, but an inordinate quantity of time is required
Solution Approach 1:
The system implements automated caption generation using encoder-decoder models that process video frames and sensor data without human intervention. The model autonomously identifies safety-critical events and generates accurate captions, eliminating the time-consuming manual labeling process while maintaining high precision through multi-modal data fusion.
Solution Approach 2:
The system performs preliminary detection of safety-critical events using sensor data (acceleration, GPS, gyroscope) before initiating full video captioning. This preliminary action filters out normal events, allowing the system to focus computational resources only on critical frames, thereby reducing overall processing time while maintaining accuracy for important events.
3Reliability
If comprehensive video analysis is performed to capture all safety-critical events, then caption accuracy improves, but computing resource consumption increases
Solution Approach 1:
The system segments event detection into two categories: safety-critical events requiring comprehensive analysis with sensor fusion, and normal events using lightweight processing. This segmentation enables high detection accuracy for critical events while maintaining overall processing efficiency through selective application of resource-intensive methods.
Solution Approach 2:
The system dynamically adjusts processing parameters based on real-time sensor data. When anomalies are detected in acceleration, GPS, or gyroscope data, the system activates comprehensive analysis mode with full sensor fusion and detailed captioning. During normal operation, it switches to efficient processing mode, optimizing the balance between detection accuracy and processing efficiency.
Data Source
AI summary
A device may receive a video and corresponding sensor information associated with a vehicle, and may extract feature vectors associated with the corresponding sensor information and an appearance and a geometry of another vehicle captured in the video. The device may generate a tensor based on the feature vectors, and may process the tensor, with a convolutional neural network model, to generate a modified tensor. The device may select a decoder model from a plurality of decoder models, and may process the modified tensor, with the decoder model, to generate a caption for the video based on attributes associated with the video. The device may perform one or more actions based on the caption for the video.