Autonomous Driving Technology Revolution : From SLAM+DL to BEV+Transformer (Part 2)

Transformer is a neural network model based on the attention mechanism, which was proposed by Google in 2017. Different from traditional RNN and CNN, Transformer mines the connection and correlation of different elements in the sequence through the attention mechanism, so that it can adapt to inputs of different lengths and structures. Transformer first achieved great success in the field of natural language processing (NLP) and was then applied to computer vision (CV) tasks with remarkable results. This top-down method reverses the construction process of BEV, uses the Transformer’s global perception ability to query corresponding information from the features of multiple perspective images, and fuses and updates the information into the BEV feature map. Tesla has adopted this top-down approach in its FSD Beta software visual perception module and demonstrated more technical ideas about BEVFormer [5] on Tesla AI-Day [6]. <a href="https://ai.plainenglish.io/autonomous-driving-technology-revolution-from-slam-dl-to-bev-transformer-part-2-940ed247b6e1">Visit Now</a>