19 citations · 36 across the 10 of their papers we have counts for
10 papers
Looking for an out: Affordances, uncertainty and collision avoidance behavior of human drivers
Leif Johnson, Johan Engström, Aravinda Srinivasan +2
Understanding collision avoidance behavior is of key importance in traffic safety research and for designing and evaluating advanced driver assistance systems and autonomous vehicl…
Modeling Pedestrian Crossing Behavior: A Reinforcement Learning Approach with Sensory Motor Constraints
Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Yee Mun Lee +1
Understanding pedestrian behavior is crucial for the safe deployment of Autonomous Vehicles (AVs) in urban environments. Traditional pedestrian behavior models often fall into two…
Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception
Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P. P. Jokinen +2
This paper presents a model of pedestrian crossing decisions, based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bo…
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…
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…
Use of immersive virtual reality-based experiments to study tactical decision-making during emergency evacuation
Laura M. Harris, Subhadeep Chakraborty, Aravinda Ramakrishnan Srinivasan
Humans make their evacuation decisions first at strategic/tactical levels, deciding their exit and route choice and then at operational level, navigating to a way-point, avoiding c…
Modeling human road crossing decisions as reward maximization with visual perception limitations
Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P. P. Jokinen +2
Understanding the interaction between different road users is critical for road safety and automated vehicles (AVs). Existing mathematical models on this topic have been proposed b…
Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello +9
Autonomous vehicles use a variety of sensors and machine-learned models to predict the behavior of surrounding road users. Most of the machine-learned models in the literature focu…
A Utility Maximization Model of Pedestrian and Driver Interactions
Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti +2
Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situati…
Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model
Aravinda Ramakrishnan Srinivasan, Mohamed Hasan, Yi-Shin Lin +4
There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic. These models focus their learning on low-dimensional error me…