The objective of this project is to explore how an autonomous vehicle must safely respond to different classes of emergency vehicles using sound, vision and other onboard sensors.
Recording Title/Project/Date Speaker Webinar Overview Link (YouTube) Title: Micromobility Regulation Best Practices Review: Scoot Now, Regulate Later Project: Safe-D TTI-05-04 Date: March 26, 2024 Gretchen Stoeltje, J.D., Texas A&M Transportation Institute This webinar will present the scope, methodology and findings of the Safe-D project Micromobility Regulation Best Practices Review (aka Scoot Now, Regulate Later). This project sought to locate data about e-scooter crashes, their actual causes, who or what party was responsible for the cause, and who or what party might be legally liable for the cause based on a combination of statutory and contractual assignment of that liability. The … Webinar Archive
(aka Senior Mixer or SMX) Problem Older adult drivers typically experience age-related declines in sensory, cognitive, and psychomotor abilities that might affect their ability to drive safely. Automation is envisioned as a potential remedy to help these individuals continue to maintain their driving safety and mobility. However, the benefits of automated features or advanced driver assistance systems (ADAS) depend on a variety of factors including trust, acceptance, adoption, understanding, as well as usage patterns and the ability to realize the full benefits of such systems. The objective of this study was to investigate whether ADAS can benefit mobility and driving … Examining Senior Driversβ Adaptation to Mixed-Level Automated Vehicles
The project will evaluate statistical and other related methods that could simplify the analysis of the unique attributes related to safety and transportation-related big data, and present guidelines that can be used by researchers and practitioners for simplifying data analyses.
This project will investigate the influences on motor vehicle and bicyclist operations within a corridor.
Abstract The number of electric vehicles on the road increases exponentially every year. Due to the quieter nature of these vehicles when operating at low speeds, there is significant concern that pedestrians and bicyclists will be at increased risk of vehicle collisions. This research explores the detectability of six electric vehicle acoustic additive sounds produced by two sound dispersion techniques: (1) using the factory approach versus (2) an excite transducer-based system. Detectability was initially measured using on-road participant tests and was then replicated in a high-fidelity immersive reality lab. Results were analyzed through both mean detection distances and pedestrian probability … A Data Driven Approach to the Development and Evaluation of Acoustic Electric Vehicle Alerting Systems for Vision Impaired Pedestrians
Alternative Title Assessment of Work Zone Pre-Crash Scenarios Using Crowdsourced Data Abstract Pavements play a vital role in the transportation infrastructure in the United States. Damage to public road transportation infrastructure causes roadways to fail to perform as intended and increases crash risks. Road damage must be detected quickly and accurately in order to maintain roads and effectively allocate repair money. In this study, four deep-learning object detection models were used for detecting five types of pavement damages using Nexar Dashcam images. The single-shot multi-box detector (SSD) and faster region-based convolutional neural networks (Faster R-CNN) object detection models using MobileNet … Detecting Pavement Distresses Using Crowdsourced Dashcam Camera Images
This project will investigate data from multiple sources, including automated pedestrian and bicycle counters, video cameras, crash databases, and GPS/mobile applications (both active and passive monitoring), to inform bicycle and pedestrian safety improvements.
This project will examine freight and passenger railroad operational and infrastructure needs can be best considered in the development of future AV/CV system architecture.
This project aims at addressing this problem by delivering a specification for a wearable worker localization and communication system prototype that utilizes ultra wide-band (UWB) technologies to facilitate real-time threat detection and warning algorithms.