摸底地平线HSD一段式端到端的方案设计

Core Insights - The article discusses two core papers from Horizon Robotics: DiffusionDrive and ResAD, focusing on their contributions to end-to-end autonomous driving solutions [2][3]. DiffusionDrive - The overall architecture of DiffusionDrive consists of three parts: perception information, navigation information, and trajectory generation [6]. - Perception information includes dynamic/static obstacles, traffic lights, map elements, and drivable areas, emphasizing the need to convey perception tasks to planning tasks in an end-to-end manner [6]. - Navigation information is crucial for avoiding incorrect routes, especially in complex urban environments like Shanghai, where navigation challenges are significant [7]. - The core of trajectory generation is "Truncated Diffusion," which leverages fixed patterns in human driving behavior to reduce training convergence difficulty and inference noise [8]. - The article outlines a method for trajectory generation using K-Means clustering to describe common human driving behaviors, which simplifies the training process [9]. - The anchor-based trajectory generation approach reduces training difficulty and enhances real-time inference capabilities, although concerns about trajectory stability over time are raised [10]. ResAD - ResAD introduces a residual design that predicts the difference between future trajectories and their inertial extrapolations, rather than generating future trajectories directly [12]. - The residual regularization is essential for managing the increasing residuals over time, ensuring that the model focuses on the true diversity of driving behaviors [13][14]. - The design allows for different noise perturbations in the trajectory generation process, adjusting learning difficulty based on the noise applied in lateral and longitudinal directions [15]. - ResAD features a trajectory ranker that utilizes a transformer model to predict metric scores based on top-k trajectory predictions and environmental information [16]. - The regularized residual supervision effectively separates the inertial component from predictions, addressing data imbalance issues in training [17]. Conclusion - Both papers from Horizon Robotics provide valuable insights and methodologies for enhancing autonomous driving technology, encouraging further exploration and development in the field [18].