Safe-D: Safety through Disruption

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Response of Autonomous Vehicles to Emergency Response Vehicles

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.

Webinar Archive

If you would like to be notified of future Safe-D webinars, please fill out this form. Recording Title/Project/Date Speaker Webinar Overview Link (YouTube) Title: Autonomous Vehicles for Small Towns: System, Service, and Safety from Research to Practice Project: Safe-D 05-109 Date: May 4, 2023 Moderator: Dr. Marcia Ory, Texas A&M University Presenters: Dr. Wei Li, Texas A&M University Dr. Bahar Dadashova, Texas A&M Transportation Institute Kevin Roscom, Wocsor LLC Muhammad Usman, Texas A&M University As of 2021, there were 18,696 small towns in the U.S. with a population of less than 50,000. These communities typically have a low population density, Webinar Archive

Examining Senior Drivers’ Adaptation to Mixed-Level Automated Vehicles

(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 Drivers’ Adaptation to Mixed-Level Automated Vehicles

Big Data Methods for Simplifying Traffic Safety Analyses

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.

Influences on Bicyclists and Motor Vehicles Operating Speed within a Corridor

This project will investigate the influences on motor vehicle and bicyclist operations within a corridor.

A Data Driven Approach to the Development and Evaluation of Acoustic Electric Vehicle Alerting Systems for Vision Impaired Pedestrians

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

Detecting Pavement Distresses Using Crowdsourced Dashcam Camera Images

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

Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas

Abstract A significant majority of the state of the art autonomous sensing and navigation technologies rely on good lane markings or detailed 3D maps of the environment and are more suited for urban communities. On the other hand, a large number of rural roads in the U.S. do not have lane markings and have irregular boundaries. These challenges are common to many small and rural communities (SRCs), defined as an incorporated city, town, or village with a population of less than 50,000. As of 2019, there are 18,723 SRCs in the US [1,2]; these communities are sparsely connected and cover Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas

Data Mining to Improve Planning for Pedestrian and Bicyclist Safety

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.

Automated Truck Mounted Attenuator

This project will develop an automated control system for TMA vehicles using a short following distance, leader-follower control concept which will remove the driver from the at-risk TMA vehicle.