Automotive Scene Captioning With Attention Maps for Accurate Perception
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Solution Overview
Problem
Existing image capturing techniques in vehicles are limited and often inaccurate in describing automotive scenarios, failing to effectively convey environmental situations to vehicle occupants.
Innovation Solution
A perception and decision-making system that generates descriptive and perceptive messages using a vision-language model to highlight relevant traffic objects, encode images into embedding vectors, and iteratively refine text messages with automotive vocabulary to ensure accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image capturing techniques are used to describe automotive scenarios, then the system is simple and easy to operate, but the accuracy and relevance of environmental descriptions are insufficient
Solution Approach 1:
The perception system is segmented into multiple specialized modules: traffic object detection module for identifying relevant objects, attention map highlighting module for emphasizing important regions, image encoder for visual feature extraction, text encoder for linguistic feature extraction, and PLM module for message generation. Each module handles a specific aspect of the perception task, improving overall accuracy while managing complexity through functional decomposition.
Solution Approach 2:
Attention maps serve as intermediaries between the image input and the description generation process. The attention map highlighting module generates attention maps that highlight relevant traffic objects and regions, which then guide the image encoder and PLM module to focus on critical elements, thereby improving description accuracy without requiring the entire system to be fundamentally more complex.
2Loss of information
If a comprehensive image captioning system is implemented to accurately describe all environmental elements, then the descriptive accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies local quality by making different parts of the image processing pipeline have different levels of detail and attention. The attention map highlighting module selectively highlights relevant traffic objects and regions while suppressing irrelevant areas. This allows the system to process the entire image but focuses computational resources on critical regions, maintaining information completeness while reducing overall processing time.
Solution Approach 2:
The system performs partial action by focusing on generating descriptions for only the most relevant traffic objects and regions identified by the attention map, rather than attempting to describe every element in the image. The PLM module generates concise messages that capture essential environmental information without exhaustively describing all details, thereby reducing processing time while maintaining sufficient information completeness for safe operation.
3Adaptability or versatility
If general-purpose image captioning is used, then the system is easier to implement, but the descriptions lack automotive-specific relevance and precision
Solution Approach 1:
The system changes parameters by training the PLM module and attention map highlighting module specifically on automotive datasets and scenarios. The text message generation is optimized for automotive vocabulary and context, with the PLM module trained to generate messages relevant to driving situations. This parameter change in training data and model architecture provides automotive-specific relevance while building upon existing image captioning frameworks, balancing adaptability with implementation feasibility.
Data Source
AI summary
A system includes: a traffic object detection module detecting traffic objects in an environment; an attention map highlighting module generating an attention map, highlighting relevant ones of the traffic objects or regions in which the relevant ones of the traffic objects are located; an image encoder, based on the attention map, encoding an image of the environment and generating an image embedding vector; a PLM module iteratively selecting and appending text to create a text message including selecting the text based on a score, the text message being a specific description of what is perceived in the environment; a text encoder encoding a portion of the text message created thus far to generate a text embedding vector; and a module, based on the image and text embedding vectors, to score the portion to generate the score, where the PLM module is configured to update the portion based on the score.