Research & Publications

Winner of the CHI 2018 Honorable Mention Award. We propose two novel vision-based methods for cognitive load estimation and evaluate them on a large-scale dataset collected under real-world driving conditions.

Winner of the CHI 2017 Best Paper Award. We consider a dataset of real-world, on-road driving to explore the predictive power of driver glances.

An approach for verifying the identity of a smartphone user with with four biometric modalities. We evaluate the approach by collecting real-world behavioral biometrics data from smartphones of 200 subjects over a period of at least 30 days.

Traffic simulation and optimization with deep reinforcement learning. Primary goal is to make the hands-on study of deep RL accessible to thousands of students, educators, and researchers.

Large-scale real-world AI-assisted driving data collection study to understand how human-AI interaction in driving can be safe and enjoyable. The emphasis is on computer vision based analysis of driver behavior in the context of automation use.

We propose a simplification of the general gaze estimation task by framing it as a gaze region estimation task in the driving context, thereby making it amenable to machine learning approaches. We go on to describe and evaluate one such learning-based approach.

We propose a set of shared autonomy principles for designing and building autonomous vehicle systems in a human-centered way, and demonstrate these principles on a full-scale semi-autonomous vehicle.

30 external vehicle-to-pedestrian display concepts for autonomous vehicles were evaluated. Simple, minimalist displays performed best.

A method for automated synchronization of vehicle sensors using accelerometer, telemetry, audio, and dense optical flow from three video sensors.

Framework for providing human supervision of a black box AI system that makes life-critical decisions. We demonstrate this approach on two applications: (1) image classification and (2) real-world data of AI-assisted steering in Tesla vehicles.