no code implementations • 6 Feb 2024 • Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P. P. Jokinen, Antti Oulasvirta, Gustav Markkula
The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle's speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle.
no code implementations • 24 May 2023 • Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober, Gustav Markkula, Arkady Zgonnikov
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.
no code implementations • 27 Jan 2023 • Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P. P. Jokinen, Antti Oulasvirta, Gustav Markkula
Interestingly, our model's decisions are sensitive to not only the time gap, but also the speed of the approaching vehicle, something which has been described as a "bias" in human gap acceptance behavior.
no code implementations • 22 Jun 2022 • Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello, Anthony Knittel, Mohamed Hasan, Majd Hawasly, John Redford, Subramanian Ramamoorthy, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula
Even though the models' RMSE value differed, all the models captured the kinematic-dependent merging behavior but struggled at varying degrees to capture the more nuanced courtesy lane change and highway lane change behavior.
no code implementations • 21 Oct 2021 • Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti, Jac Billington, Gustav Markkula
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 situations.
no code implementations • 21 Apr 2021 • Aravinda Ramakrishnan Srinivasan, Mohamed Hasan, Yi-Shin Lin, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula
There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic.