Collaborative perception allows real-time inter-agent information exchange and thus offers invaluable opportunities to enhance the perception capabilities of individual agents. However, limited communication bandwidth in practical scenarios restricts the inter-agent data transmission volume. This implies a trade-off between perception performance and communication cost. To address this issue, we propose Which2comm, a novel multi-agent 3D object detection framework leveraging object-level sparse features. By integrating semantic information of objects into detection boxes, we introduce semantic detection boxes (SemDBs). Innovatively transmitting these object-level sparse features among agents not only significantly reduces the demanding communication volume, but also improves object detection performance. Moreover, an adaptive strategy is further proposed to select only safety-critical connected and automated vehicles (CAVs) for collaborative perception when there are multiple CAVs available, thereby maintaining stable communication costs. To validate the proposed method, a large-scale, multi-modal  dataset, Multi-V2X, is established for vehicle-to-everything (V2X) perception tasks with various CAV penetration rates. Multi-V2X comprises 146k frames with over 4.2 million 3D annotations, featuring high agent density (up to 31 agents) to evaluate perception robustness in complex traffic environments. Extensive experiments demonstrate that Which2comm consistently outperformed other state-of-the-art methods on both detection performance and communication cost, exhibiting superior robustness to real-world latency.  

In recent years, female scientists have contributed to advancements in the transportation sector through technological innovation and unique perspectives, playing pivotal roles across various domains of the field. This study analyzes 54,511 publications from 20 Science Citation Index (SCI) Q1 transportation journals (2014–2024), encompassing over 100,000 scholars, to advance the understanding of the status of female scientists in transportation academia. Female authors constitute only 22.91% of first authors and 20.86% of corresponding authors, revealing persistent underrepresentation despite incremental progress in mixed-gender collaborations. This study uses a mixed-methods framework that includes data mining, the mean normalized log-transformed citation score (MNLCS), probabilistic gender identification, keyword co-occurrence, and clustering analysis to investigate macrolevel trends and longitudinally compare four collaboration modes. The key findings include that (1) mixed-gender teams exhibit significant growth, with MNLCS exceeding single-gender teams by 0.048–0.067, and (2) female-led collaborations exhibit a stronger tendency to drive sustained exploration in research fields. These findings support gender-equality policies and guide early-career scholars in collaboration strategies and frontier tracking, promoting inclusive development in transportation research.

Understanding how drivers perceive and respond to external stimuli in driving tasks is important for the development of advanced driving technologies and human-computer interaction. In this paper, we conducted a temporal response analysis between driving data and cortical activation data measured by functional near-infrared spectroscopy (fNIRS), based on a naturalistic driving experiment. Temporal response function analysis indicates that stimuli, which elicit significant responses of drivers include distance, acceleration, time headway, and the velocity of the preceding vehicle. For these stimuli, the time lags and response patterns were further discussed. The influencing factors on drivers’ perception were also studied based on various driver characteristics. These conclusions can provide guidance for the construction of car-following models, the safety assessment of drivers and the improvement of advanced driving technologies.