arXiv subjects

Zhaobo Zheng

Publications and source records attributed to Zhaobo Zheng.

5 recordsLinked to original sources

arXiv2026

Adaptive Driving Style for SAE Level-2 Driving Automation: Minimizing Preference Mismatch

Driving style is a key factor in the comfort and acceptance of automated vehicle (AV) features. In SAE Level-2 automation, where the driver must supervise the system and remain ready to intervene, mismatches between the automation's driving style and the driver's preference can reduce trust and trigger takeovers. This paper proposes an adaptive driving-style control framework that minimizes such preference mismatch. In a driving-simulator study, we compare fixed, trust-based, and preference-based adaptation heuristics and analyze their effects on preference mismatch and trust. We then train a driving-preference prediction model and use it in an implicit adaptation policy that selects among bounded driving styles for upcoming events. A validation study shows that the predictive policy achieves equal or lower preference mismatch than comparison baselines, particularly when starting from a less defensive style, while also yielding higher average trust. The results provide a step toward developing human-aware driving automation that can implicitly adapt its driving style to the driver's preferences.

cs.HC↗

arXiv2024

How is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-physiological Signals

Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popular in development, research, and operation. When compared to traditional fixed-wing aircraft and rotorcraft, VTOLs bring unique challenges as they combine many maneuvers from both types of aircraft. Pilot workload is a critical factor for safe and efficient operation of VTOLs. In this work, we conduct a user study to collect multimodal data from 28 pilots while they perform a variety of VTOL flight tasks. We analyze and interpolate behavioral patterns related to their performance and perceived workload. Finally, we build machine learning models to estimate their workload from the collected data. Our results are promising, suggesting that quantitative and accurate VTOL pilot workload monitoring is viable. Such assistive tools would help the research field understand VTOL operations and serve as a stepping stone for the industry to ensure VTOL safe operations and further remote operations.

cs.HC↗

arXiv2024

DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services

A hybrid society is expected to emerge in the near future, with different mobilities interacting together, including cars, micro-mobilities, pedestrians, and robots. People may utilize multiple types of mobilities in their daily lives. As vehicle automation advances, driver modeling flourishes to provide personalized intelligent services. Thus, modeling drivers across mobilities would pave the road for future society mobility-as-a-service, and it is particularly interesting to predict driver behaviors in newer mobilities with traditional mobility data. In this work, we present takeover prediction on a micro-mobility, with car simulation data.The promising model performance demonstrates the feasibility of driver modeling across mobilities, as the first in the field.

cs.HC↗

arXiv2024

Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles

Humans' internal states play a key role in human-machine interaction, leading to the rise of human state estimation as a prominent field. Compared to swift state changes such as surprise and irritation, modeling gradual states like trust and satisfaction are further challenged by label sparsity: long time-series signals are usually associated with a single label, making it difficult to identify the critical span of state shifts. Windowing has been one widely-used technique to enable localized analysis of long time-series data. However, the performance of downstream models can be sensitive to the window size, and determining the optimal window size demands domain expertise and extensive search. To address this challenge, we propose a Selective Windowing Attention Network (SWAN), which employs window prompts and masked attention transformation to enable the selection of attended intervals with flexible lengths. We evaluate SWAN on the task of trust prediction on a new multimodal driving simulation dataset. Experiments show that SWAN significantly outperforms an existing empirical window selection baseline and neural network baselines including CNN-LSTM and Transformer. Furthermore, it shows robustness across a wide span of windowing ranges, compared to the traditional windowing approach.

cs.HC↗

arXiv2023

Trust in Shared Automated Vehicles: Study on Two Mobility Platforms

The ever-increasing adoption of shared transportation modalities across the United States has the potential to fundamentally change the preferences and usage of different mobilities. It also raises several challenges with respect to the design and development of automated mobilities that can enable a large population to take advantage of this emergent technology. One such challenge is the lack of understanding of how trust in one automated mobility may impact trust in another. Without this understanding, it is difficult for researchers to determine whether future mobility solutions will have acceptance within different population groups. This study focuses on identifying the differences in trust across different mobility and how trust evolves across their use for participants who preferred an aggressive driving style. A dual mobility simulator study was designed in which 48 participants experienced two different automated mobilities (car and sidewalk). The results found that participants showed increasing levels of trust when they transitioned from the car to the sidewalk mobility. In comparison, participants showed decreasing levels of trust when they transitioned from the sidewalk to the car mobility. The findings from the study help inform and identify how people can develop trust in future mobility platforms and could inform the design of interventions that may help improve the trust and acceptance of future mobility.

cs.HC↗