ESR14 – Computational AV/Cyclist Interaction Models

Popular scientific abstract

Cycling is growing as an active mode of transport among European countries and it is vital to provide a safe environment for their movement. Cyclists and pedestrians are considered vulnerable road users (VRUs) that they need extra attention since they don’t have metal protection like vehicles. With the rapid development of automated vehicles (AVs), it is essential to predict VRUs’ behavior in scenarios in which they interact with AVs.

In this research project, we aim at developing quantitative models to predict cyclists’ behavior at unsignalzied intersections. Different modeling approaches have been deployed to describe cyclists’ behavior and develop predictive models for their application in AVs. These predictive models will help AVs to interact safely and comfortably with cyclists at conflicting scenarios like unsignalized intersections.

who am I?

I am Ali Mohammadi. I come from Iran. My professional interests are behavioral modeling, traffic safety, connected and autonomous vehicles, and traffic simulation.


My host university is the Chalmers University of Technology in Sweden. I work at the vehicle safety division at the Department of Mechanics and maritime sciences.

Supervisor: Professor Marco Dozza,

Co-supervisor: Professor Gustav Markkula,


I hold a bachelor’s degree in civil engineering from the University of Bojnord, and a master’s degree in highway and transportation engineering from Amirkabir University of Technology, Iran (Tehran Polytechnic). In my master’s project, I worked on developing lane-changing models for their application in traffic simulation packages.


The overall goal of the project is to observe the mechanism of interaction between cyclists and vehicles at the intersection. In this regard, with the help of data in hand, we want to propose computational interaction models to predict the behavior of cyclists when they are interacting with vehicles. Cyclists follow certain states when they are approaching an intersection at the same time as a vehicle. And our goal is to predict these states by our models. The bicycle simulation dataset along within-site datasets will be used to determine the procedure of interaction between these two parties. Cyclists use visual cues to communicate their intent in the interaction, so knowing and understanding these visual cues would be useful for automated vehicles to predict the next state of the cyclists. As a result, automated vehicles can react safely when they are encountering cyclists.


Cyclists along with pedestrians are vulnerable road users that have less protection compared to motorized vehicles in traffic flow. And the crash reports have been saying that frequent accidents with cyclists are happening at the intersections where the two paths cross each other. It is crucial to mitigate these kinds of crashes and reduce the fatalities and injuries either by advanced driving systems or by proposing recommendations for the geometry design of intersections. And, on the other hand, we are living in the era of developing automated vehicles, the vehicles that should decide on their own how to react and respond in the presence of other road users and in particular cyclists. in this project, we aim at developing behavioral models for cyclists to be used in automated vehicles so that automated vehicles could predict the behavior of cyclists at intersections. In this way, we mitigate the risk of collision and provide a safe and reliable framework for the movement of vulnerable road users.

Research description

To address the research objectives for this project, we performed three experiments to understand the mechanism of interaction between cyclists and vehicles at intersections:

Study 1: field data collection. In this study, we collected interaction events from a real intersection in Gothenburg, Sweden. this data was used to determine the important factors affecting cyclists crossing decisions.

Study 2: bike simulator experiment. in this experiment, we used a bike simulator to recreate the interaction scenario with an AV at an unsignazlied intersection. In this study, we tested some important parameters (visibility conditions and time to arrival at the intersection) on cyclists’ behavior.

Study 3: driving simulator (ongoing). in this experiment, we aim at analyzing drivers’ behavior when encountering cyclists at an unsignalzied intersection. this data was collected at TME during my secondment.


we aim at using the results of this study for the safety assessment of automated vehicles. with a better understanding of the interaction mechanism between these two agents we can achieve higher accuracy in predicting human behavior and consequently providing safe underlying systems for their movement.

My publication


References to publications with links, as they need to be open access.