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IAMCV Logo

Interaction of Autonomous and Manually Controlled Vehicles

"Developing basic knowledge for future technological development." 

FWF Project Details

Project number: P 34485
Project lead: Univ. Prof. Dr. Cristina Olaverri-Monreal
Decision board: 08.03.2021

The early days of highly autonomous vehicles co-existing with traditional vehicles could present road safety risks and create unanticipated consequences as other drivers may be surprised by the different behavior of autonomous cars. Manually-controlled vehicles might expect that vehicles with automated capabilities behave in a similar way. To this end, autonomous vehicles algorithms need to take into account human behavior and if appropriate, be able to reproduce it. The “Interaction of autonomous and manually-controlled vehicles (IAMCV)” project will study the best strategies to ensure that manual driven vehicles understand the decisions made by autonomous vehicles and vice versa. To this end, it will focus on investigating the actions that occur between vehicles and humans and that influence one another by observing road utilization. This knowledge will make possible to provide automation algorithms with the necessary data sets to make decisions that consider the actions that occur between vehicles and humans and that have an effect upon one another.

JKU-ITS vehicle, side view

The key objectives of IAMCV are:

O1) To find out how to show the information related to the decisions performed by the automated system in an efficient way to other road users.

O2) To tailor the automation according to the human behavior by studying intervehicular interaction and being able to predict the response of other road users.

O3) Promote trust in automation by demonstrating the technology as a reliable and safe.

These goals will be achieved by analyzing road users' behavior from naturalistic driving and floating car data from vehicles equipped with sensors. Different levels of automation will be considered focusing on awareness and diagnosis of previously defined road situations. Several scenarios will be replicated under real and lab conditions (i.e. intersections with multiple vehicles and roundabouts) to perform the pertinent experiments and determine the impact of the coexistence of AVs and other road users on general road safety. Information related to the situation will be conveyed by the automation to manual drivers to determine if this knowledge generates a degree of confidence that can compensate mistrust.

The IAMCV project will help to provide a solid scientific base for advancements in automated vehicles technology, building knowledge and expertise in the field of ITS; It will contribute to extending the standards and regulations for designing autonomous vehicles, considering road user needs aiming at increasing user acceptance and trust in Autonomous Vehicle’s (AV) by guaranteeing road safety.

List of Research Papers:

JKU-ITS Automobile for research on autonomous vehicles.

Certad, N., Morales-Alvarez, W., Novotny, G., and Olaverri-Monreal, C., in Artificial Intelligence and Data Mining for Intelligent Transportation Systems and Smart Mobility, EUROCAST 2022, Las Palmas de Gran Canaria, Canary Islands, Spain, 2022.

Automated Driving Systems: Impact of Haptic Guidance on Driving Performance after a Take Over Request Interaction of Autonomous and Manually-Controlled Vehicles: Implementation of a Road User Communication Service

Smirnov, N., Tschernuth, S., Morales-Alvarez W., and Olaverri-Monreal, C., IEEE Intelligent Vehicle Symposium 2022, Aachen, Germany 2022, https://arxiv.org/abs/2204.13643

Nikita Smirnov Vladimirovic, MSc.

is one of the two Ph.D. students working on the IAMCV project. As a software engineer, he worked on commmunication systems, web applications and game-theory. 

Novel Certad Hernandez, MSc.

is one of the Ph.D students working on the IAMCV project. He has taught electronic engineering, robotics and signal processing as an assistant professor.

Sebastian Tschernuth, BSc.

is a Master student and helps the Ph.D students with their work.

Delgermaa Gankhuyag, BSc. 

is a Master student working on the project. She will attain her masters degree in Statistics and focusses her work on data analysis.


 

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