Efficient Joint Resource Allocation for Wireless Powered ISAC with Target Localization

Wireless powered integrated sensing and communication (ISAC) faces a fundamental tradeoff between energy supply, communication throughput, and sensing accuracy. This paper investigates a wireless powered ISAC system with target localization requirements, where users harvest energy from wireless power transfer (WPT) and then conduct ISAC transmissions in a time-division manner. In addition to energy supply, the WPT signal also contributes to target sensing, and the localization accuracy is characterized by Cramér-Rao bound (CRB) constraints. Under this setting, we formulate a max-min throughput maximization problem by jointly allocating the WPT duration, ISAC transmission time allocation, and transmit power. Due to the nonconvexity of the resulting problem, a suitable reformulation is developed by exploiting variable substitutions and the monotonicity of logarithmic functions, based on which an efficient successive convex approximation (SCA)-based iterative algorithm is proposed. Simulation results demonstrate convergence and significant performance gains over benchmark schemes, highlighting the importance of coordinated time-power optimization in balancing sensing accuracy and communication performance in wireless powered ISAC systems.

FocalAD: Local Motion Planning for End-to-End Autonomous Driving

In end-to-end autonomous driving,the motion prediction plays a pivotal role in ego-vehicle planning. However, existing methods often rely on globally aggregated motion features, ignoring the fact that planning decisions are primarily influenced by a small number of locally interacting agents. Failing to attend to these critical local interactions can obscure potential risks and undermine planning reliability. In this work, we propose FocalAD, a novel end-to-end autonomous driving framework that focuses on critical local neighbors and refines planning by enhancing local motion representations. Specifically, FocalAD comprises two core modules: the Ego-Local-Agents Interactor (ELAI) and the Focal-Local-Agents Loss (FLA Loss). ELAI conducts a graph-based ego-centric interaction representation that captures motion dynamics with local neighbors to enhance both ego planning and agent motion queries. FLA Loss increases the weights of decision-critical neighboring agents, guiding the model to prioritize those more relevant to planning. Extensive experiments show that FocalAD outperforms existing state-of-the-art methods on the open-loop nuScenes datasets and closed-loop Bench2Drive benchmark. Notably, on the robustness-focused Adv-nuScenes dataset, FocalAD achieves even greater improvements, reducing the average colilision rate by 41.9% compared to DiffusionDrive and by 15.6% compared to SparseDrive.