| Title/Project/Date | Speaker | Webinar Overview |
|---|---|---|
| Title: Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data
Project: Safe-D VTTI-00-028 Date: August 26, 2021, 14:00 EST Zoom Link: Register Here |
|
The high-frequency, high-resolution telematics driving data provide valuable information on both long-term driver behavior as well as instantaneous driving conditions. This project uses telematics driving data to modeling driving risk with three primary objectives: 1) characterize the high-frequency kinematic signatures for safety-critical events; 2) modeling driver level crash risk prediction based on kinematics features, and 3) instantaneous crash risk assessment.
The research team proposes a state-of-the-art approach for characterizing the high-frequency kinematic signatures. We developed several features representing driver behavior and underlying driving risk. These features were applied to several large-scale ride-hailing data and naturalistic driving study data to predict driving risk. The high-frequency kinematic data coupled with the rarity of crashes demand novel modeling approaches. We developed deep learning-based models and variational inference-based rare event modeling to predict crashes from normal driving as well as predict high-risk drivers. A convolutional neural network and long short-term memory network is developed to predict crashes, near-crashes, and normal stopping behaviors. We developed a novel Variational Information for Extremal (VIE) framework for modeling rare events through deep learning models. This project addressed key methodological challenges in predicting driving risk using high-frequency telematics data. The findings of the project will benefit driving data processing at scale, driver safety management program, and real-time risk prediction. |
| Title: Data Mining Twitter to Improve Automated Vehicle Safety
Project: Safe-D 04-098 Date: June 22, 2021, 13:00 EST Zoom Link: Register Here |
Dr. Bert Huang |
Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. Prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. This webinar will discuss a novel three-step process that not only measures this impact but also translates it into a set of guidelines for reporting on AV events designed to calibrate driver trust and expectations. The steps include the development of a novel search method to identify AV-relevant user comments on Twitter via a semi-supervised learning approach. A topic modeling and sentiment analysis of the identified tweets to analyze the influence of crashes and news events on user sentiment about AVs, and a mixed-methods analysis to translate these findings into guidelines for AV reporting. |
