127 citations · 288 across the 53 of their papers we have counts for
10 papers · 1 filter
A merging interaction model explains human drivers' behaviour from input signals to decisions
Olger Siebinga, Arkady Zgonnikov, David Abbink
One of the bottlenecks of automated driving technologies is safe and socially acceptable interactions with human-driven vehicles, for example during merging. Driver models that pro…
Data-Driven Semi-Supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection
Yongqi Dong, Lanxin Zhang, Haneen Farah +2
Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the ac…
Merging in a Coupled Driving Simulator: How do drivers resolve conflicts?
Olger Siebinga, Arkady Zgonnikov, David A. Abbink
Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on me…
A cognitive process approach to modeling gap acceptance in overtaking
Samir H. A. Mohammad, Haneen Farah, Arkady Zgonnikov
Driving automation holds significant potential for enhancing traffic safety. However, effectively handling interactions with human drivers in mixed traffic remains a challenging ta…
Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention
Frederik S. B. Westerhout, Julian F. Schumann, Arkady Zgonnikov
Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles.…
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study
Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober +2
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable m…
Feasible Action-Space Reduction as a Metric of Causal Responsibility in Multi-Agent Spatial Interactions
Ashwin George, Luciano Cavalcante Siebert, David Abbink +1
Modelling causal responsibility in multi-agent spatial interactions is crucial for safety and efficiency of interactions of humans with autonomous agents. However, current formal m…
The COMMOTIONS Urban Interactions Driving Simulator Study Dataset
Aravinda Ramakrishnan Srinivasan, Julian Schumann, Yueyang Wang +7
Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is…
A model of communication-enabled traffic interactions
O. Siebinga, A. Zgonnikov, D. A. Abbink
A major challenge for autonomous vehicles is handling interactive scenarios, such as highway merging, with human-driven vehicles. A better understanding of human interactive behavi…
TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction
Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora +2
Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this…