Core Viewpoint - The article discusses the WHALES dataset, which aims to enhance cooperative perception and scheduling in autonomous driving, addressing the limitations of existing single-vehicle systems in non-line-of-sight scenarios [2][3][4]. Group 1: WHALES Dataset Overview - WHALES (Wireless enHanced Autonomous vehicles with Large number of Engaged agentS) is the first large-scale dataset designed for evaluating communication perception agent scheduling and scalable cooperative perception in vehicular networks [4]. - The dataset integrates detailed communication metadata and simulates real-world communication bottlenecks, providing a rigorous standard for evaluating scheduling strategies [4]. - WHALES includes 70,000 images, 17,000 frames of LiDAR data, and over 2.01 million 3D annotations, making it a comprehensive resource for research in cooperative driving [14][29]. Group 2: Key Features and Contributions - The dataset supports V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure) perception, optimizing the CARLA simulator for speed and computational cost, achieving an average of 8.4 cooperative agents per driving scenario [14][29]. - WHALES introduces a novel Coverage-Aware Historical Scheduler (CAHS) algorithm, which prioritizes agents based on historical coverage, outperforming existing methods in perception performance [4][19]. - The dataset allows for the evaluation of various scheduling algorithms, including Full Communication, Closest Agent, and the proposed CAHS, enhancing the understanding of cooperative perception tasks [19][27]. Group 3: Experimental Results - Experiments conducted on the WHALES dataset demonstrated that cooperative models significantly outperform standalone models in 3D object detection, with F-Cooper improving mAP by 19.5% and 38.4% at 50m and 100m detection ranges, respectively [25]. - The CAHS algorithm showed superior performance in both single-agent and multi-agent scheduling scenarios, indicating its effectiveness in enhancing cooperative driving safety [27][28]. - The dataset's design allows for a linear increase in time cost with the addition of agents, making it feasible for large-scale simulations [14][29].