Cooperation among human-driven vehicles (HVs) is essential for traffic safety and efficiency. However, the emergence of autonomous vehicles (AVs) has prompted a new question: Will HVs still cooperate with AVs? Prior studies and narrative reviews yielded inconsistent findings. To answer this question, we conducted the first systematic review and meta-analysis of HV–AV cooperation, synthesizing evidence from 24 articles, 27 samples, 32 effect sizes, and 5,778 participants. Results revealed that people drive less cooperatively when interacting with AVs than with HVs (Hedges’ g = − 0.19, 95% CI [ − 0.31, − 0.07]). The meta-regression revealed a significant link between cooperative driving and the year of publication, with more recent studies showing more cooperation; other moderators (e.g., data collection methods) were not significant. We discuss the implications of less cooperation for AV development, traffic regulations, and human–AI cooperation, and current challenges in theory, replicability, and ecological validity, in addition to offering recommendations for future research.

While autonomous vehicles (AVs) could transform transportation, their “black box” nature often leaves occupants unaware of the rationale for their actions. Providing explanations can enhance transparency and facilitate widespread AV adoption. This study investigated scenario (when) and human factors (who) that influence the Demand of Explanations (DoE) to ensure explanations are provided when needed, followed by exploring the reasons (why) behind these demands. We conducted an online experimental study among 440 participants, who viewed 36 simulated driving scenarios, varying in AV actions, driving styles, time/weather and traffic environments. Results of multilevel and qualitative analysis showed that: (1) DoE was significantly higher when AVs drove aggressively, in urban areas, during turning and merging, and in nighttime or rain; (2) participants who had lower trust in AVs and older adults significantly demanded more explanations; and (3) safety and traffic rules were the primary reasons for seeking explanations.

This overview presents one of the cup challenges of IEEE BigData 2024, with the topic of suicide risk level detection on social media posts. Given a training set of N = 2000 posts (N = 500 labelled and N = 1500 unlabelled posts) from r/SuicideWatch subreddits, the task of this challenge is to develop a predictive model capable of classifying the suicidal posts into four levels (i.e., indicator, ideation, behaviour, and attempt). The dataset provided simulated the obstacles existed in relevant fields (e.g., model overfitting, data scarcity and class imbalance), participating teams are supposed to tackle these issues while exploring the effectiveness of various model architectures. We received submissions from 21 teams and works of 13 teams underwent final evaluation. Teams addressed key challenges in suicide risk detection including limited suicidal data and suicidal risk imbalance. They employed novel approaches to overcome these obstacles, leveraging a diverse range of models from foundational base language models (BLMs) to state-of-the-art large language models (LLMs). In the competition, the highest weighted F1-score achieved under the final evaluation was 0.7605. The findings of this challenge can provide technical implications to social media suicide detection and contribute the clinical effectiveness to the applications of machine learning in digital suicide or mental healthcare management.