Core Insights - The article discusses the resurgence of end-to-end (E2E) perception in the autonomous driving industry, highlighting its impact on the field and the shift from traditional modular approaches to more integrated solutions [4][5][9]. Group 1: End-to-End Revival - End-to-end is not a new technology; it was initially hoped to directly use neural networks to output trajectories from camera images, but stability and safety were issues [9]. - The traditional architecture of localization, perception, planning, and control has been the mainstream approach, but advancements in BEV perception and Transformer architectures have revived end-to-end methods [9]. - Companies are now exploring various one-stage and two-stage solutions, with a focus on neural network-based planning modules [9]. Group 2: Perception Benefits in End-to-End - In traditional frameworks, perception aimed to gather as much accurate scene information as possible for planning, but this modular design limited the ability to meet planning needs [11]. - Current mainstream end-to-end solutions continue to follow this approach, treating various perception tasks as auxiliary losses [13]. - The key advantage of end-to-end is the shift from exhaustive perception to "Planning-Oriented" perception, allowing for a more efficient and demand-driven approach [14][15]. Group 3: Navigation-Guided Perception - The article introduces a Navigation-Guided Perception model, which suggests that perception should be guided by navigation information, similar to how human drivers focus on relevant scene elements based on driving intent [16][18]. - A Scene Token Learner (STL) module is proposed to efficiently extract scene features based on BEV characteristics, integrating navigation information to enhance perception [18][19]. - The SSR framework demonstrates that only 16 self-supervised queries can effectively represent the necessary perception information for planning tasks, significantly reducing the complexity compared to traditional methods [22]. Group 4: World Models and Implicit Supervision - The article discusses the potential of world models to replace traditional perception tasks, providing implicit supervision for scene representation [23][21]. - The SSR framework aims to enhance understanding of scenes through self-supervised learning, predicting future BEV features to improve scene query comprehension [20][21]. - The design allows for efficient trajectory planning while maintaining consistency for model convergence during training [20]. Group 5: Performance Metrics - The SSR framework outperforms various state-of-the-art (SOTA) methods in both efficiency and performance, achieving significant improvements in metrics such as L2 distance and collision rates [24]. - The framework's design allows for a reduction in the number of queries needed for effective scene representation, showcasing its scalability and efficiency [22][24].