BEV Vision Transformer Adaptive Resolution via Token Halting
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Solution Overview
Problem
Existing autonomous vehicle (AV) systems face inefficiencies in processing bird's-eye view (BEV) representations due to fixed resolution settings that do not adapt to varying contextual features, leading to suboptimal computational costs and accuracy needs.
Innovation Solution
Adaptive inference resolution techniques using Vision Transformers (ViT) that dynamically adjust the number of processed tokens based on contextual features like weather, speed, and road conditions, employing a self-attention mechanism to discard irrelevant image patches, thereby optimizing computational efficiency while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed resolution settings are used for processing BEV representations, then device complexity is reduced and ease of operation is improved, but productivity decreases and computational efficiency is suboptimal
Solution Approach 1:
The system dynamically adjusts the resolution of BEV representations based on contextual features such as weather conditions, vehicle speed, and road conditions. Instead of using fixed resolution settings, the processing system adapts the number of tokens (patches) to process in real-time, optimizing computational efficiency for varying driving scenarios while managing complexity through adaptive algorithms.
2Measurement precision
If high resolution BEV representations are processed, then measurement precision and reliability are improved, but use of energy and computational load increase
Solution Approach 1:
The system changes the resolution parameter of BEV representations dynamically based on contextual features. When detection accuracy requirements are high (e.g., complex road conditions, high speed), the system processes higher resolution tokens. When conditions are simpler, it reduces resolution to lower computational energy consumption, thus adapting the parameter to balance precision and energy use.
Solution Approach 2:
The system applies different processing resolutions to different regions or aspects of the BEV representation based on their importance and contextual relevance. Critical regions requiring high detection accuracy maintain higher resolution, while less critical areas use lower resolution, optimizing the balance between measurement precision and computational energy across the entire scene.
3Use of energy by moving object
If low resolution BEV representations are processed, then use of energy and computational load are reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The system dynamically adjusts resolution based on contextual features that indicate safety-critical scenarios. When contextual features suggest high-risk conditions (e.g., adverse weather, high speed, complex intersections), the system automatically increases processing resolution to maintain reliability and safety accuracy, while using lower resolution only when safety conditions permit energy optimization.
Data Source
AI summary
A method includes dividing an image into a plurality of patches; generating for the patches image tokens each including a halting score associated with the image token; generating a contextual features token representative of at least one contextual feature in connection with the image; processing the image tokens and the contextual features token using a vision transformer block; performing adaptive halting on the image tokens output from the transformer block, the adaptive halting comprising updating the halting scores associated with the image tokens and discarding ones of the image tokens having halting scores greater than or equal to a predetermined threshold score, wherein the non-discarded image tokens comprise remaining image tokens; and forwarding the remaining image tokens to a next processing block.