Search Results for author: Gustav Markkula

Found 11 papers, 2 papers with code

Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception

no code implementations6 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.

Decision Making

Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study

no code implementations24 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.

Decision Making

Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings

no code implementations17 Apr 2023 Chi Zhang, Amir Hossein Kalantari, Yue Yang, Zhongjun Ni, Gustav Markkula, Natasha Merat, Christian Berger

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving.

Model Selection regression

An active inference model of car following: Advantages and applications

no code implementations27 Mar 2023 Ran Wei, Anthony D. McDonald, Alfredo Garcia, Gustav Markkula, Johan Engstrom, Matthew O'Kelly

We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models.

Decision Making

Modeling human road crossing decisions as reward maximization with visual perception limitations

no code implementations27 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.

Reinforcement Learning (RL)

Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?

no code implementations22 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.

Autonomous Vehicles

A Utility Maximization Model of Pedestrian and Driver Interactions

no code implementations21 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.

Maneuver-based Anchor Trajectory Hypotheses at Roundabouts

1 code implementation22 Apr 2021 Mohamed Hasan, Evangelos Paschalidis, Albert Solernou, He Wang, Gustav Markkula, Richard Romano

Accordingly, our model employs a set of maneuver-specific anchor trajectories that cover the space of possible outcomes at the roundabout.

Decoder motion prediction +1

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

no code implementations26 Mar 2020 Fanta Camara, Nicola Bellotto, Serhan Cosar, Florian Weber, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Gustav Markkula, Anna Schieben, Fabio Tango, Natasha Merat, Charles W. Fox

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets.

Autonomous Driving Descriptive

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