Core Insights - The article discusses the latest trends and research directions in the field of autonomous driving, highlighting the integration of multimodal large models and vision-language action generation as key areas of focus for both academia and industry [2][5]. Group 1: Research Directions - The research community is concentrating on several key areas, including the combination of MoE (Mixture of Experts) with autonomous driving, benchmark development for autonomous driving, and trajectory generation using diffusion models [2]. - The closed-loop simulation and world models are emerging as critical needs in autonomous driving, driven by the limitations of real-world open-loop testing. This approach aims to reduce costs and improve model iteration efficiency [5]. - There is a notable emphasis on performance improvement in object detection and OCC (Occupancy Classification and Counting), with many ongoing projects exploring specific pain points and challenges in these areas [5]. Group 2: Notable Projects and Publications - "ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation" is a significant project from Huazhong University of Science and Technology and Xiaomi, focusing on integrating vision and language for action generation in autonomous driving [5]. - "All-in-One Large Multimodal Model for Autonomous Driving" is another important work from Zhongshan University and Meituan, contributing to the development of comprehensive models for autonomous driving [6]. - "MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding" from Chongqing University aims to enhance understanding of driving scenarios through multimodal analysis [8]. Group 3: Simulation and Reconstruction - The project "Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images" from TUM focuses on advanced reconstruction techniques for autonomous driving [14]. - "CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving" from Fraunhofer IVI and TU Munich is another notable work that addresses dynamic scene reconstruction [16]. Group 4: Trajectory Prediction and World Models - "Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics" from Hong Kong University of Science and Technology and Didi emphasizes the importance of trajectory prediction in autonomous driving [29]. - "World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model" from the Chinese Academy of Sciences focuses on developing a comprehensive world model for autonomous driving [32].
又有很多自动驾驶工作中稿了ICCV 2025,我们发现了一些新趋势的变化...
自动驾驶之心·2025-08-16 00:03