Improving Methods to Measure Attentiveness through Driver Monitoring

Researchers at the Virginia Tech Transportation Institute (VTTI) worked on a project sponsored by the Safety Through Disruption (Safe-D) National University Transportation Center (UTC) to improve algorithms that measure a driver\u2019s attention level in real time, leveraging pre-existing data collected during a private (proprietary) study for General Motors (GM). Driver inattention poses a significant problem on today\u2019s roadways, increasing risk for all road users. In 2020, an estimated 3,142 lives were lost due to distracted driving. This is most likely an underrepresentation of the number of lives lost, as distracted driving is only determined through self-reports and accounts from witnesses. Driver monitoring systems (DMS) have the potential to identify when a driver is distracted and refocus their attention back to the forward roadway. However, the potential safety impact of these systems depends on how accurately they can differentiate between distracted and attentive driving

Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas

69A3551747115/ Project 06-004A significant majority of state-of-the-art autonomous sensing and navigation technologies rely on good lane markings or detailed 3D maps of the environment, making them more suited for urban communities. Conversely, many 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), which are sparsely connected and cover huge areas. The objective of this project was to develop an efficient sensing and navigation system for SRCs that uses crowdsourced topological maps, such as OpenStreetMap, and provides high-level road network information in concert with onboard sensing systems that include lidar and cameras to localize and navigate an autonomous vehicle. The system was tested and validated on rural roads in an SRC around Bryan, TX

Sensor Degradation Detection Algorithm for Automated Driving Systems

69A3551747115/Project VTTI-00-034The project developed a sensor degradation detection algorithm for Automated Driving Systems (ADS). Weather, cyberattacks, and sensor malfunction can degrade sensor information, resulting in significant safety issues, such as leading the vehicle off the road or causing a sudden stop in the middle of an intersection. From the Virginia Tech Transportation Institute\u2019s (VTTI\u2019s) Naturalistic Driving Database (NDD), 100 events related to sensor perception were selected to establish baseline sensor performance. VTTI determined performance metrics using these events for comparison in simulation. A virtual framework was used to test degraded sensor states and the detection algorithm\u2019s response. Old Dominion University developed the GPS model and collaborated with the Global Center for Automotive Performance Simulation (GCAPS) to develop the degradation detection algorithm utilizing the DeepPOSE algorithm. GCAPS created the virtual framework, developed the LiDAR and radar sensor models, and executed the simulations. The sensor degradation detection algorithm will aid ADS vehicles in decision making by identifying degraded sensor performance. The detection algorithm achieved 70% accuracy. Additional training methods and adjustments are needed for the accuracy level required for vehicle system implementation. The process of collecting sensor data, creating sensor models, and utilizing simulation for algorithm development are major outcomes of the research

Response of Autonomous Vehicles to Emergency Response Vehicles (RAVEV)

69A3551747115The objective of this project was to explore how an autonomous vehicle identifies and safely responds to emergency vehicles using visual and other onboard sensors. Emergency vehicles can include police, fire, hospital and other responders\u2019 vehicles. An autonomous vehicle in the presence of an emergency vehicle must have the ability to accurately sense its surroundings in real-time and be able to safely yield to the emergency vehicle. This project used machine learning algorithms to identify the presence of emergency vehicles, mainly through onboard vision, and then maneuver an in-path non-emergency autonomous vehicle to a stop on the curbside. Two image processing frameworks were tested to identify the best combination of vision-based detection algorithms, and a novel lateral control algorithm was developed for maneuvering the autonomous vehicle

Analysis of Advanced Driver-Assistance Systems in Police Vehicles

69A3551747115/[TTI-05-02]Motor vehicle crashes are the leading cause of death for police officers. Advanced driver assistance systems (ADAS) have the potential to improve officer safety by removing some of the driver\u2019s vehicle control responsibilities. This project included two phases: (1) an ADAS needs and implementation analysis in police vehicles; and (2) an evaluation of police ADAS in a driving simulation study. The first phase included a systematic review of literature and an online survey with officers to understand their ADAS needs and current systems in police vehicles. The second phase evaluated ADAS in high-demand situations using a high-fidelity driving simulator. Results indicated that officer behaviors and opinions on ADAS features were influenced by the trust officers had in the available ADAS, as well as other key factors such as ADAS training and perceived usefulness. ADAS features, including forward collision warning, automatic emergency braking, and blind spot monitoring had a positive effect on police officers' driving performance and in reducing workload. The outcomes of this project provide guidelines regarding effective ADAS features/types to automotive companies supplying police vehicles and can improve officer safety in police operations

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

69A3551747115/Project 05-086The 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 exciter 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 of detection. This research aims to verify the lab environment in order to allow for a broader range of potential test scenarios, more repeatable tests, and faster test sessions. Along with pedestrian drive-by tests, supplemental experiments were conducted to evaluate stationary vehicle acoustics, 10 and 20 km/h drive by acoustics, and interior acoustics of each additive sound

Introduction to Communications in Transportation

69A3551747115/Project: 06-008As new Intelligent Transportation Systems (ITS) and vehicle-to-everything (V2X) communication technology and protocols continue to emerge, additional training is needed for personnel working in the transportation sector. The Virginia Department of Transportation has already created a training program focusing on general topics pertaining to connected and automated vehicles (CAVs) and has recently identified a need for a more specific program focusing on communication technologies. To address this need, the Virginia Tech Transportation Institute team developed a 60-minute online learning program that includes a series of 10 narrated modules with slides, images, charts, videos, and learning assessments. The training provides a high-level overview of the types of communications that support ITS, traffic management, and connected vehicle environments. The training includes descriptions of the communication technologies, protocols, performance metrics, use cases, and data security. The included communication technologies are currently being utilized by infrastructure owner-operators (IOOs), original equipment manufacturers (OEMs), and industry technology providers

Cooperative Perception of Connected Vehicles for Safety

69A3551747115/ Project 05-115In cooperative perception, reliably detecting surrounding objects and communicating the information between vehicles is necessary for safety. However, vehicle-to-vehicle transmission of huge datasets or images can be computationally expensive and often not feasible in real time. A robust approach to ensure cooperation involves relative pose estimation between two vehicles sharing a common field of view. Detecting the object and transferring its location information in real time is necessary when the object is not in the ego vehicle\u2019s field of view. In such scenarios, reliable and robust pose recovery of the object at each instant ensures the ego vehicle accurately estimates its trajectory. Once pose recovery is established, the object\u2019s location information can be obtained for future trajectory prediction. Deterministic predictions provide only point estimates of future states which is not trustworthy under dynamic traffic scenarios. Estimating the uncertainty associated with the predicted states with a certain level of confidence can lead to robust path planning. This study proposed quantifying this uncertainty during forecasting using stochastic approximation, which deterministic approaches fail to capture. The current method is simple and applies Bayesian approximation during inference to standard neural network architectures for estimating uncertainty. The predictions between the probabilistic neural network models were compared with the standard deterministic models. The results indicate that the mean predicted path of probabilistic models was closer to the ground truth when compared with the deterministic prediction. The study has been extended to multiple datasets, providing a comprehensive comparison for each model

Signal Awareness Applications

69A3551747115/Project VTTI-00-021Intersection collisions account for 40% of all crashes on U.S. roadways. It is estimated that 165,000 accidents, which result in approximately 800 fatalities annually, are due to vehicles that pass through intersections during red signal phases. Although infrastructure-based red-light violation countermeasures have been deployed, intersections remain a top location for vehicle crashes. The Virginia Department of Transportation and its research arm, the Virginia Transportation Research Council, partnered with the Virginia Tech Transportation Institute to create the Virginia Connected Corridors (VCC), a connected vehicle test bed located in Fairfax and Blacksburg, Virginia, that enables the development and assessment of early-stage connected and automated vehicle applications. Recently, new systems have been deployed that transmit position correction messages to support lane-level accuracy, enabling development of signal awareness applications such as red-light violation warning. This project enhances the current capabilities of VCC platforms by developing new signal awareness safety and mobility features. Additionally, this project investigated the technical and human factors constraints associated with user interfaces for notifying and alerting drivers to pertinent intersection-related information to curb unsafe driving behaviors at signalized intersections

Private 5G Technology and Implementation Testing

69A3551747115/06-006NEC developed a Video Analytics implementation for traffic intersections using 5G technology. This implementation included both hardware infrastructure and software applications supporting 5G communications, which allows low latency and secure communications. The Virginia Tech Transportation Institute (VTTI) worked with NEC to facilitate the usage of a 3,400- to 3,500-MHz program experimental license band without SAS integration to successfully implement a private 5G deployment at the VTTI Smart Road intersection and data center. Specific use cases were developed to provide alerting mechanisms to both pedestrians and vehicles using cellular vehicle-to-everything/PC5 technology when approaching a traffic intersection and a dangerous situation is detected