Multi-Agent collaboration addresses inherent limitations of individual agent systems, including limited sensing range and occlusion-induced blind spots. Despite significant progress, persistent challenges such as constrained communication bandwidth and under-explored subsequent extensions still hinder real-time deployment and further developments of collaborative autonomous driving systems. In this work, we propose ZeRCP, a unified communication-efficient framework that bridges collaborative perception with future scene prediction. Specifically, (i) we devise a plug-and-play request-free spatial filtering module (ZeroR) that eliminates the reliance on request maps while preserving inter-agent spatial complementarity modeling. This approach further reduce communication latency and bandwidth consumptions. (ii) We design a multi-scale pyramidal prediction network anchored by a novel Spatial-Temporal Deformable Attention (STDA) module, extending frame-wise detection to multi-frame predictions. This method adeptly models spatiotemporal dynamics without relying on auto-regressive recursion. We evaluate our method on a large-scale dataset in challenging semantic segmentation and scene prediction tasks. Extensive experiments demonstrate the superiority and effectiveness of ZeRCP in bandwidth-constrained collaboration scenarios and spatiotemporal prediction applications.

We study how the design of testing institutions, encompassing both the tests themselves and the procedures used to administer them, shapes selection outcomes in environments with multiple criteria and strategic agents. We model the testing agency as either a set of independent bureaucracies (each test administered separately) or a joint bureaucracy (where test order and personalization can be coordinated). Our mechanism design analysis shows that under a joint bureaucracy, fixed-order sequential mechanisms with stringent tests are optimal for maximizing the probability mass of qualified candidates selected. Furthermore, we demonstrate that personalizing tests through upfront communication, now increasingly feasible via AI and automation, can select all qualified candidates. Finally, we compare institutional settings and quantify the value of controlling test order, showing that the benefit depends critically on the distribution of testees and the stringency of optimal tests. Our results contribute to the design of robust, efficient, and fair testing systems in both human and AI-mediated environments.

Despite the widespread implementation, the Adaptive Cruise Control (ACC) systems still fall short in delivering a satisfactory human-likely experience, primarily due to the heterogeneity in driving experience preferences and the unpredictable, heterogeneous nature of human driving behaviors. To address this critical gap, we introduce an innovative driving style-aware car-following model that effectively captures the varying cut-in tendencies of adjacent vehicles by utilizing the Maximum Entrop Inverse Reinforcement Learning (Max-Ent IRL) method. A distinct reward function is developed to replicate human driving behavior, which can achieve a harmonious equilibrium between efficiency, safety, and comfort. The efficacy of this model is rigorously evaluated through a comprehensive analysis on car-following episodes extracted from the Next Generation Simulation (NGSIM) I-80 dataset. A novel human-likely metric is utilized for evaluating the performance of the proposed model in comparison to standard benchmarks. The results demonstrably favor our approach, showing notable enhancements in efficiency, safety, and comfort. Additionally, the model’s versatility is confirmed by its ability to accommodate a wide spectrum of driving styles, as evidenced by the diverse weights learned from different driving styles. These findings highlight the significant potential of our model in advancing ACC technology for more human-oriented vehicular systems that align closely with the natural driving instincts and preferences of humans.