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Found 11 papers, 2 papers with code
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
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.
Comparing merging behaviors observed in naturalistic data with behaviors generated by a machine learned model
There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic.
Maneuver-based Anchor Trajectory Hypotheses at Roundabouts
Accordingly, our model employs a set of maneuver-specific anchor trajectories that cover the space of possible outcomes at the roundabout.
Maneuver-Aware Pooling for Vehicle Trajectory Prediction
Behavior of each of the surrounding vehicles is governed by the motion of its neighbor vehicles.
A Utility Maximization Model of Pedestrian and Driver Interactions
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.
Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
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.
Modeling human road crossing decisions as reward maximization with visual perception limitations
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.
An active inference model of car following: Advantages and applications
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.
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving.
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study
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.
Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception
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.
