Motor vehicle crashes are the leading cause of deaths for police officers. These crashes have been mainly attributed to the use of in-vehicle technologies while driving. Advanced driver-assistance systems (ADAS) have the potential to improve officer safety by removing some of driver vehicle control responsibilities. Although current ADAS in police vehicles can adapt to emergencies and provide multi-modal alerts, there has been little research on how ADAS can reduce driving task demands in situations that officers are also engaged in secondary tasks while driving. The objective of this project was to evaluate ADAS in police vehicles. This project investigated ADAS features in situations of multi-tasking and the types of ADAS that are most effective for improving driver safety. This project included two phases including (1) ADAS needs and implementation analysis in police vehicles; and (2) 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. The outcomes provide guidelines to automotive companies supplying police vehicles regarding effective ADAS features/types and can improve officer safety in police operations.
- Advanced driver-assistance systems (ADAS) can substantially improve the driving performance and reduce workload of law enforcement officers.
- Officer’s opinion on ADAS features were influenced by the trust officers had in the available ADAS systems among other key factors such as ADAS training and perceived usefulness.
EWD & T2 Products
Student Impact Statement(pdf): This file contains statements from three students as to the impact this project had on education and workforce development.
The ADAS recommendations based on the findings of Phase 1 are posted on the Safe-D researcher portal and Dr. Zahabi’s research laboratory website here.
The PI organized several open houses and lab tours inviting middle school, high school, and undergraduate students to visit her lab and learn about human factors in transportation. Survey responses from about 50 students attending these events indicated that these activities helped the students become familiar with the human factors area (average rating of 4.4/5) and better understand the applications of computing and driving simulations in transportation (average rating of 4.5/5).
In Fall 2021, the PI and her students gave an on-site demonstration to high school students to become familiar with the human-systems engineering area and its applications in transportation. The students learned how to use eye-tracking glasses to capture eye movements to study distraction and workload of drivers.
In summer 2022, the lab associated with this project provided training for a K-12 teacher to familiarize them with the human-computer interaction area and its applications in transportation. These activities included (1) demonstration of the high-fidelity driving simulator and training on creating simple scenarios using graphical user interface and Java; and (2) data collection and analysis of physiological data using wearable devices including eye-tracking glasses and heart rate monitors.
Undergraduate engineering student training: This project provided research training for three undergraduate students. Two of these students worked on this project as their TTI internships during the summer 2020 and 2021. The students were trained on conducting systematic literature review, design of driving simulation-based experiments, data processing, data analysis, and manuscript writing.
Graduate student training: This project provided funding and training for a PhD student and provided the basis for their PhD dissertation study (Dissertation Title: Modeling and Analysis of Advanced Driver Assistance Systems in Police Vehicles). They defended their PhD dissertation in May 2023.
Nasr, V., Wozniak, D., Shahini, F., & Zahabi, M. (2021). Application of advanced driver-assistance systems in police vehicles. Transportation research record, 2675(10), 1453-1468. (Link: https://journals.sagepub.com/doi/pdf/10.1177/03611981211017144)
Wozniak, D., Shahini, F., Nasr, V., & Zahabi, M. (2021). Analysis of advanced driver assistance systems in police vehicles: A survey study. Transportation research part F: traffic psychology and behaviour, 83, 1-11. (Link: https://www.sciencedirect.com/science/article/pii/S1369847821002217)
Shahini, F., Nasr, V., Wozniak, D., & Zahabi, M. (2022, September). Law enforcement officers’ acceptance of advanced driver assistance systems: An application of technology acceptance modeling (TAM). In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 66, No. 1, pp. 325-329). Sage CA: Los Angeles, CA: SAGE Publications. (Link: https://journals.sagepub.com/doi/pdf/10.1177/1071181322661071)
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/J3D2AK
Research Investigators (PI*)
Start Date: 2020-05-01
End Date: 2022-12-30
Grant Number: 69A3551747115
Total Funding: $199,970
Source Organization: Safe-D National UTC
Project Number: TTI-05-02
Safe-D Theme Areas
Safe-D Application Areas
Driver Factors and Interfaces
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Texas A&M University
Texas A&M Transportation Institute
College Station, Texas 77843-3135