Driver's Interactions with Advanced Vehicles in Various Traffic Mixes and Flows (connected and autonomous vehicles (CAVs) electric vehicles (EVs), V2X, trucks, bicycles and pedestrians) - Phase I: Driver Behavior Study and Parameters Estimation

Project Abstract

Considering the rapid boom in information technology and people's increasing dependence on mobile data, automotive manufacturers have started equipping vehicles with wireless communication capabilities, manufacturing what are commonly known as connected vehicles, and autonomous systems to assist drivers with certain driving tasks. These technological advances have led to fast tracking the deployment of connected and autonomous vehicles (CAVs), and an increased momentum in implementing these applications, as the number of driving assistance systems pre-equipped in cars by automotive companies has witnessed a sharp increase during this decade. However, most of the new cars come pre-equipped with these applications, which means that the drivers' reactions to such applications are not fully examined since most of the experiments involving these applications are done using microscopic simulations with the behavior of the drivers' being assumed. Therefore, this rapid deployment and implementation has led to a lack of research in understanding the drivers' reactions to such applications before they actually use them, which is an essential element in ensuring the effectiveness and successful implementations of such applications.

This study aims to investigate driver behavior in terms of braking, steering and throttle control and change in speed, in the presence of CAV applications, using a driving simulator. The study consisted of 93 participants from diverse socio-economic backgrounds who drove in 186 experiments. The use of Pedestrian Collision Warning and Red-Light Violation Warning had a significant impact on participant braking behavior, where participants resorted to initial aggressive braking in the presence of these applications. Forward Collision Warning had a positive influence on change in speed while Curve Speed Warning had no impact on speed. Lastly, the steering wheel and throttle Take Over Reaction time (TORt) in the post autonomous mode being 2.47 seconds and 2.98 seconds respectively, is greatly influenced by the annual miles driven, age, and familiarity with this technology. Based on the findings, certain driver-related parameters were identified; TORt, Deceleration Rate and Change in Speed, which could be integrated into a traffic simulator to simulate realistic human driving behavior in mixed traffic, involving both human drivers as well as automated vehicles.

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Impacts and Outcomes

Cars equipped with new technology -- connected and automated vehicles -- will help increase mobility and safety. But how will human drivers react to and interact with such technology? This study analzyed driver behavior while using five different CAV applications in a driving simulator and used real-world data to validate it. The data revealed that with the exception of one system, the curve warning system, these systems improve safety and driving performance.

Universities and Sponsoring Organizations Involved

Morgan State University, Maryland Department of Transportation State Highway Administration, U.S. Department of Transportation Office of the Secretary-Research

Principal Investigator(s)

Dr. Mansoureh Jeihani, Dr. Snehanshu Banerjee, Md. Muhib Kabir, Nashid K. Khadem

Funding Sources and Amounts

USDOT: $50,000 (Federal), Morgan State University: $19,680 (Match), State Highway Administration: $6,075 (Match)

Completion Date

January 2020


Autonomous and connected vehicles, electric vehicles, Complete Streets, multimodal transportation, mobility, safety