Silent automation failures, where a system fails to detect a hazard without warning, pose a critical safety challenge for partially automated vehicles. While research has mostly focused on takeover requests, how to support a driver in silent failure remains underexplored. We conducted a multi-modal driving simulator study with 48 participants to investigate how different Prospective Situation Awareness Enhancement (PSAE) interfaces, delivered via augmented reality head-up display, affect takeover performance. By integrating behavioral, subjective psychological, and physiological data, our analysis suggests that situational awareness (SA) serves as an important moderating factor through which PSAE interfaces improve takeover performance. Further, we found that providing perceptual cues was most effective in enhancing SA, while communicating system intent was superior for building trust. Finally, we identified a potential correlate of SA in the neuroactivity. Overall, this paper contributes to understanding how transparency-oriented interfaces may support drivers and provides design insights into HMI design for silent failures.
While autonomous vehicles (AVs) continue to advance and reshape modern transportation, they remain unable to navigate all traffic conditions without human input, highlighting the need for remote human intervention in edge-case scenarios. Two major teleoperation paradigms have emerged to address this need: tele-driving and tele-assistance. In tele-driving, remote operators (ROs) continuously control the AV through direct access to its actuators. In tele-assistance, ROs provide high-level instructions through a specialized interface, with low-level maneuvers delegated to the AV. We conducted a quantitative comparison of these paradigms, examining four edge-case scenarios: one uses a steering wheel and pedals, the other employs discrete high-level commands through a Wizard-of-Oz methodology. We measured mental workload, situation awareness, time completion, and overall user experience (UX). Results indicate that the tele-assistance interface reduced operators’ mental workload and improved situation awareness, suggesting the need for further development of tele-assistance interfaces.
Road rage poses great risks to road safety. Digital interventions show promising potential in regulating rage-related maladaptive behaviors to mitigate such risks. This requires a clear understanding of road rage dynamics. Unlike prior work using artificial scenarios, we build the first-of-its-kind Real Road Rage Footage ($R^3$-$Ftg$) dataset and recreate some of its most risky and anger-inducing scenes in both audiovisual and simulated environments. Then we recruit 52 participants to experience those scenes and record their behavioral and physiological responses. We find: (1) Road rage has been successfully induced, and simulation provides more realistic experiences. (2) "Slow-rise, fast-decay" phenomena are observed in both environments, which can be interpreted by Parasympathetic rebound. (3) This process can be modeled using second-order damped oscillation distributions. To our knowledge, we are the first to model road rage dynamics in authentic scenarios recreated from real-world events, enabling a holistic understanding on road rage.
The next step for In-vehicle Conversational Assistants (IVCAs) will be their capability to initiate and automate proactive system interactions throughout journeys. However, diverse drivers make it challenging to design voice interventions tailored towards individual on-road expectations. This paper evaluates the effectiveness of Human-in-the-Loop (HITL) Multi-Objective Bayesian Optimization (MOBO) in design by implementing ProVoice: a Virtual Reality (VR) driving simulator integrating MOBO to investigate the effects of IVCA design variants on perceived mental demand, predictability, and usefulness. By reporting the Pareto Front from a within-subjects VR study (N=19), this paper proposes optimal design trade-offs. Follow-up analysis demonstrates MOBO’s success in discovering effective intervention strategies, with reduced participant mental demand, alongside enhanced predictability and usefulness while engaging with the proactive IVCA. Implications for computational techniques in future research on proactive intervention strategies are discussed. ProVoice can extend to include alternative design parameters and driving scenarios, encouraging intervention design on a broad scale.
People's transportation choices reflect complex trade-offs shaped by personal preferences, social norms, and technology acceptance. Predicting such behavior at scale is a critical challenge with major implications for urban planning and sustainable transport. Traditional methods use handcrafted assumptions and costly data collection, making them impractical for early-stage evaluations of new technologies or policies. We introduce Generative Traffic Agents (GTA) for simulating large-scale, context-sensitive transportation choices using LLM-powered, persona-based agents. GTA generates artificial populations from census-based sociodemographic data. It simulates activity schedules and mode choices, enabling scalable, human-like simulations without handcrafted rules. We evaluate GTA in Berlin-scale experiments, comparing simulation results against empirical data. While agents replicate patterns, such as modal split by socioeconomic status, they show systematic biases in trip length and mode preference. GTA offers new opportunities for modeling how future innovations, from bike lanes to transit apps, shape mobility decisions.
Human drivers’ control quality in the first seconds after a handover is critical to shared-driving safety; potentially unsafe steering or pedal inputs therefore require detection and correction by the automated vehicle’s safety-fallback system. Yet performance in this window is vulnerable because cognitive states fluctuate rapidly, causing purely rationality-driven, cognition-unaware models to miss early control dynamics. We present an interpretable driver model grounded in bounded rationality with online adaptation that predicts early-stage control quality. We encode boundedness by embedding cognitive constraints in reinforcement learning and adapt latent cognitive parameters in real time via particle filtering from observations of driver actions. In a vehicle-in-the-loop study (n=41), we evaluated predictive performance and physiological validity. The adaptive model not only anticipated hazardous takeovers with higher coverage and longer lead times than non-adaptive baselines but also demonstrated strong alignment between inferred cognitive parameters and real-time eye-tracking metrics. These results confirm that the model captures genuine fluctuations in driver risk perception, enabling timely and cognitively grounded assistance.