Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles

Zheng, Zhaobo; Akash, Kumar; Misu, Teruhisa; Krishmoorthy, Vidya; Dong, Miaomiao; Lee, Yuni; Huang, Gaojian

doi:10.1145/3536221.3556637

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…As automated vehicle (AV) and automated driver assistance systems (ADAS) advance, the driver-vehicle interaction is of high importance to safety, driver trust, and acceptance [5,24,25]. Besides research on driver fatigue, distraction, and driving styles [1,15,38], driver takeovers in AV remain critical because it is a fundamental interaction related to safety and user trust [3,35]. Takeover prediction through non-invasive in-vehicle sensors can enhance driving safety and improve user experiences.…”

Section: Introductionmentioning

confidence: 99%

DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services

Zheng,

Akash,

Misu

2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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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. CCS CONCEPTS• Human-centered computing → User models; • Computing methodologies → Neural networks.

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