从地平线自动驾驶2025年的工作，我们看到了HSD的野心......

Core Insights - Horizon is advancing in the autonomous driving sector by focusing on large-scale production of the new HSD system and reshaping the foundational logic of autonomous driving through cutting-edge research papers [2][3] - The company is transitioning from a technology supplier to a standard-defining entity in the industry, supported by capital influx following its Hong Kong listing [2] Group 1: End-to-End Autonomous Driving - ResAD introduces a normalized residual trajectory modeling framework that simplifies the learning task and enhances model performance, achieving a PDMS score of 88.6 in NAVSIM benchmark tests [8] - CorDriver enhances safety in end-to-end autonomous driving by explicitly defining safe passage areas, resulting in a 66.7% reduction in collision rates with traffic participants [11] - TTOG unifies motion prediction and path planning tasks, demonstrating a 36.06% reduction in average L2 error on the nuScenes dataset [15] - MomAD addresses trajectory prediction consistency and stability issues by introducing momentum mechanisms, showing significant improvements in collision rates and trajectory smoothness [19] - GoalFlow generates high-quality multimodal trajectories by using precise target point guidance, achieving a PDMS score of 90.3 in NavSim benchmark tests [22] - RAD employs a large-scale 3DGS-based reinforcement learning framework to enhance safety, reducing collision rates by three times compared to pure imitation learning methods [26] - DiffusionDrive utilizes a truncated diffusion model for real-time end-to-end autonomous driving, achieving an 88.1 PDMS score and significantly improving planning quality [30] Group 2: Autonomous Driving Scene Generation & World Models - Epona is a self-regressive diffusion world model that achieves high-resolution, long-term future scene generation and trajectory planning, outperforming existing methods in the NuScenes dataset [33] - UMGen generates diverse, multimodal driving scenes, supporting user-controlled scenario generation and demonstrating superior authenticity and controllability compared to existing methods [38] - DrivingWorld constructs a world model for autonomous driving via a video GPT framework, generating high-fidelity videos with strong temporal consistency and structural integrity [41] Group 3: Autonomous Driving VLM & VLA - AlphaDrive integrates reinforcement learning and reasoning into visual language models for high-level planning in autonomous driving, improving planning accuracy by 25.52% compared to standard fine-tuning models [45] - The company has established a community of nearly 4,000 members and over 300 autonomous driving companies and research institutions, focusing on various autonomous driving technology stacks [49]