While advanced driving assistant systems (ADAS) can offer significant benefits to driving safety, driver behaviors are still critical to driving safety, especially in hazardous scenarios. Though ADAS can handle most of the operational driving tasks, they are still less capable of understanding evolving traffic scenarios and hence are less defensive compared to human drivers. Further, most existing studies focused on the influence of ADAS on the behaviors of passenger vehicles, but the safety of long-haul trucks is of more concern. Thus, it becomes imperative to understand how ADAS affects driver behaviors, especially defensive driving behaviors, among long-haul truck drivers. To address this research gap, a naturalistic driving experiment among long-haul truck drivers was conducted, where 868 right-side-merging events that can allow defensive driving behaviors were extracted. Drivers’ defensive driving behavior decision (i.e., yes or no), defensive driving behavior type (i.e., which type of defensive driving behavior), and time to defensive driving behavior were modeled. Results show that ADAS can facilitate drivers’ defensive driving behaviors, as indicated by a higher percentage of actions and quicker responses when approaching hazardous areas. Further, drivers’ defensive driving behaviors (likelihood and types) changed with the progress of the drive, drowsiness levels, traffic conditions, and ADAS availability. Findings from this study provide insights into understanding truck drivers’ behaviors in the context of driving automation and guide the design of future driver training programs for ADAS users, human-machine interfaces of ADAS, and laws or regulations regarding the adoption of ADAS in long-haul trucks.

Users increasingly expect conversational agents (CAs) to communicate effectively by adjusting language strategies to different contexts and providing personalized responses. Using a 2 (context: service vs. emergency) × 3 (second-person pronoun usage: informal “you,” formal “you,” vs. no pronoun) between-subjects design, this study investigated how second-person pronouns used by CAs influenced users’ perceptions through an online survey with 1242 valid responses. To facilitate the customized design of CAs, demographic factors (e.g., age, gender) are also considered. Results indicated that subtle shifts in pronoun usage triggered significant differences in psychological consequences, with both forms of “you” providing fewer benefits than omitting pronouns across contexts. Users in the emergency context exhibited lower purchase willingness than those in the service context. Age, gender, and education also significantly predicted users’ perceptions. Overall, the findings highlight the psychological impacts of second-person pronouns and inform the design of interaction strategies for CAs.

As a novel AI-powered conversational system, large language models (LLMs) have the potential to be used in various applications. Recent advances in LLMs like ChatGPT have made LLM-based academic tools possible. However, most of the existing studies on the adoption of LLM for academic tasks were based on theoretical or qualitative analyses, which failed to provide empirical evidence on the effects of LLMs on users’ behaviors. Additionally, although previous work has investigated users’ acceptance of conventional conversational systems, little is known about how scholars evaluate LLMs when they are used for academic tasks. Hence, we conducted an empirical field experiment to assess the performance of 48 early-stage scholars on two core academic activities (paper reading and literature reviews) under varying time constraints. Prior to the tasks, participants underwent different training programs about LLM capabilities and limitations. Then, we built a hierarchy dependency network using the Bayesian network. Statistical regression analyses were further conducted to quantify relationships among influential factors of task performance and users’ attitudes toward the LLMs. It was found that young scholars have upheld relatively high academic integrity when using LLMs for academic tasks, and user-LLM performance varied with the task type and time pressure but not with the type of training we used. Further, scholars’ traits can also affect their performance in academic tasks and attitudes towards the LLMs. This work can inspire the future development of LLM-related user training and guide the optimization of LLMs.

Electric vehicles (EVs), though becoming increasingly popular, raise concerns about motion sickness (MS). However, few studies have explored the causes of and solutions to MS in EVs. The regenerative braking (RB), as a unique function in EVs, is believed to cause MS but has not been validated. Thus, we investigated the effect of RB on MS development and explored whether providing auditory motion cues can mitigate MS among passengers. An on-road study with 16 participants who are susceptible to MS was conducted, with the level of RB (low- versus high-level) and auditory motion cues (presence versus absence) as the within-subject factor. Our results confirmed that higher levels of RB can induce MS. Further, providing auditory motion cues can mitigate MS when high-level RB was used. The findings highlight the importance of motion cues in EVs and provide insights into the design of RB systems and corresponding human-machine interaction strategies.

Drivers’ appropriate mental models of and trust in advanced driver assistance systems (ADAS) are essential to driving safety in vehicles with ADAS. Although several previous studies evaluated drivers’ ADAS mental models of and trust in adaptive cruise control and lane-keeping assist systems, research gaps still exist. Specifically, recent developments in ADAS have made more advanced functions available but they have been under-investigated. Furthermore, the widely adopted proportional correctness-based scores may not differentiate drivers’ objective ADAS mental model and subjective bias toward the ADAS. Finally, most previous studies adopted only regression models to explore the influential factors and thus may have ignored the underlying association among the factors. Therefore, our study aimed to explore drivers’ mental models of and trust in emerging ADAS by using the sensitivity (i.e.,d’) and response bias (i.e.,c) measures from the signal detection theory. We modeled the data from 287 drivers using additive Bayesian network (ABN) and further interpreted the graph model using regression analysis. We found that different factors might be associated with drivers’ objective knowledge of ADAS and subjective bias toward the existence of functions/limitations. Furthermore, drivers’ subjective bias was more associated with their trust in ADAS compared to objective knowledge. The findings from our study provide new insights into the influential factors on drivers’ mental models of ADAS and better reveal how mental models can affect trust in ADAS. It also provides a case study on how the mixed approach with ABN and regression analysis can model observational data.