most citedMultimodal Driver State Modeling through Unsupervised Learning

1 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC20221 cited

Rethinking infrastructure design: Evaluating pedestrians and VRUs' psychophysiological and behavioral responses to different roadway designs

Xiang Guo, Austin Angulo, Arash Tavakoli +3

The integration of human-centric approaches has gained more attention recently due to more automated systems being introduced into our built environments (buildings, roads, vehicle…

cs.HC2022

The Impact of Surrounding Road Objects and Conditions on Drivers Abrupt Heart Rate Changes

Arash Tavakoli, Arsalan Heydarian

Recent studies have pointed out the importance of mitigating drivers stress and negative emotions. These studies show that certain road objects such as big vehicles might be associ…

cs.HC20221 cited

Driver State Modeling through Latent Variable State Space Framework in the Wild

Arash Tavakoli, Steven Boker, Arsalan Heydarian

Analyzing the impact of the environment on drivers' stress level and workload is of high importance for designing human-centered driver-vehicle interaction systems and to ultimatel…

cs.HC20211 cited

Multimodal Driver State Modeling through Unsupervised Learning

Arash Tavakoli, Arsalan Heydarian

Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high a…

cs.HC2021

Driver State and Behavior Detection Through Smart Wearables

Arash Tavakoli, Shashwat Kumar, Mehdi Boukhechba +1

Integrating driver, in-cabin, and outside environment's contextual cues into the vehicle's decision making is the centerpiece of semi-automated vehicle safety. Multiple systems hav…