Core Insights - The article highlights the latest advancements in autonomous driving technologies, focusing on various research papers and frameworks that contribute to the field [2][3]. Multimodal Models & VLA - ORION presents a holistic end-to-end framework for autonomous driving, utilizing vision-language instructed action generation [5]. - An all-in-one large multimodal model for autonomous driving is introduced, showcasing its potential applications [6][7]. - MCAM focuses on multimodal causal analysis for ego-vehicle-level driving video understanding [9]. - AdaDrive and VLDrive emphasize self-adaptive systems and lightweight models for efficient language-grounded autonomous driving [10]. Simulation & Reconstruction - ETA proposes a dual approach to self-driving with large models, enhancing efficiency through forward-thinking [13]. - InvRGB+L introduces inverse rendering techniques for complex scene modeling [14]. - AD-GS and BézierGS focus on object-aware scene reconstruction and dynamic urban scene reconstruction, respectively [18][19]. End-to-End & Trajectory Prediction - Epona presents an autoregressive diffusion world model for autonomous driving, enhancing trajectory prediction capabilities [25]. - World4Drive introduces an intention-aware physical latent world model for end-to-end autonomous driving [30]. - MagicDrive-V2 focuses on high-resolution long video generation for autonomous driving with adaptive control [35]. Occupancy Networks - The article discusses advancements in 3D semantic occupancy prediction, highlighting the transition from binary to semantic data [44]. - GaussRender and GaussianOcc focus on learning 3D occupancy with Gaussian rendering techniques [52][54]. Object Detection - Several papers address 3D object detection, including MambaFusion, which emphasizes height-fidelity dense global fusion for multi-modal detection [64]. - OcRFDet explores object-centric radiance fields for multi-view 3D object detection in autonomous driving [69]. Datasets - The ROADWork Dataset aims to improve recognition and analysis of work zones in driving scenarios [73]. - Research on driver attention prediction and motion planning is also highlighted, showcasing the importance of understanding driver behavior in autonomous systems [74][75].

汽车辅助驾驶已经逐渐成为现代汽车的重要组成部分。它不仅能够提升驾驶的安全性，还能为驾驶员提供更加人性化的驾驶体验。而这一切都离不开感 知技术的支持。那么，汽车辅助驾驶究竟是如何通过感知技术来实现其功能的呢？ 感知方案都有哪些？ 蝙蝠通过超声波听声点位，人类通过眼睛和耳朵来感知空间，而在汽车上，则通过各种传感器进行感知。目前市场主流的组合辅助驾驶主要是 V+R+L+U的感知组合，其中R代表毫米波雷达，V代表摄像头，L代表激光雷达，U代表超声波雷达，此外，车内的车载驾驶员监控（DMS）也算作一个摄 像头。 举例来说，以比亚迪的天神之眼C为例，其配备了5个毫米波雷达与12个摄像头，包括3颗800万前视摄像头、4颗300万环视摄像头、4颗300万侧视摄像 头、1颗300万后视摄像头，也就是5R12V感知方案。 V+R+L+U们看到的世界是什么样的？ 以奔驰为例，其前视采用双目摄像头。双目摄像头通过两个摄像头同时感知前方物体，并利用视差算法计算物体距离，从而实现较为精准的测距功能。 BEV鸟瞰图 举例来说，通过多个摄像头采集图像后融合，系统将采集图像通过矫正后，统一输入到神经网络来提取特征，然后通过注意力机制的神经网络 ...