Katie Driggs-Campbell

Current Research

Given the current capabilities of autonomous vehicles, one can easily imagine autonomous vehicles being released on the road in the near future. However, it can be assumed that this transition will not be instantaneous, suggesting two key points:

(1) levels of autonomy will be introduced incrementally (e.g. active safety systems as currently released), and
(2) autonomous vehicles will have to be capable of driving in a mixed environment, with both humans and autonomous vehicles on the road.

In both of these cases, the human driven vehicle (or generally the human-in-the-loop system) must be modeled in an accurate and precise manner that is easily integrated into control frameworks.

Our developed driver modeling framework estimates the empirical reachable set, which is an alternative look at a classic control theoretic safety metric. This allows us to:

  • predict driving behavior over long time horizons with very high accuracy

  • apply intervention schemes for semi-autonomous vehicles

  • mimic nuanced interactions between humans and autonomy in cooperative maneuvers

Experimental Design for Human-in-the-Loop Driving Simulations

I've been working on setting up the experimental platform for human driving studies and control applications. The platform used is a Force Dynamics Car Simulator, shown on the left, to conduct driving experiments. This has been integrated with PreScan and dSPACE sytems. This allows for a realistic driving experience, while allowing complete control of the test in a safe environment.

Car Simulator 

Safe Interaction with Autonomous Vehicles

Utilizing driver modeling methods to predict future driver action in particular scenarios, we experiment with how autonomous vehicles interact with surrounding human drivers on the road. The goal is to create a new methodology for path planning and high level control decisions for an autonomous vehicle that must interact and collaborate in a heterogeneous environment. This information can be communicated or measured by the autonomous vehicle of interest to plan control strategries for safe interaction.
The focuses of this work have considered:

  • estimating and predicting discrete modes of behavior in vehicles

  • predicting human driver responses to conveyed intent

  • generating human-like trajectories using optimization based planning to match interaction metrics

Improving Trust in Automation through Intelligent UI Design

When people interact with automation, there must be a clear line of communciation to ensure understanding. This line of research focuses on the development of user interfaces to better inform the user of what the automation is doing. Specifically, we have looked at internal vs. external information and how to optimally design a user interface using information theory.
Through this, we have found:

  • by effectively conveying external information, we can:

    • improve driving performance while handing off control between the automation and the human driver

    • increase overall trust in the automation

    • improve situational awareness

  • by modeling the UI as a communication channel, we can adapt the UI to an individual tendancies and balance brevity and utility in an optimal manner

Human-in-the-Loop Control for Semi-Autonomous Cars

To create a smart active safety system for semi-autonomous vehicles, a human-in-the-loop control system has been developed, utilizing driver modeling to accurately predict the actions of the driver. This looks at driver monitoring to determine when the human is distracted and/or has enough situational awareness to safely control the vehicle. Key components incorporate computer vision, machine learning, sensor fusion, and control theory. Extensions include probabilistic driver modeling to assess threat and applying formal verification and model checking quantify driver performance.

Undergraduate Research

Environmental Sensors

Development of Wearable, Real-Time Sensors for Personal Exposure and Health Analysis

Advisors: Dr. NJ Tao and Dr. Erica Forzani
Funded by NIH to quantify VOC exposure on a personal level with a device that is cost efficient, portable, selective, and sensitive. Other projects included particle detection and metabolic breath analyzers.

Development of Conductivity and Temperature Sensors for Geological Studies

Mentor: Dr. Hongyu Yu
The goal of this project was to design, build, and test sensor arrays containing conductivity and temperature sensors for data collection in hot springs to characterize microenvironments.