Learning an interpretable model for driver behavior prediction with inductive biases
To plan safe maneuvers and act with foresight, autonomous vehicles must be capable of
accurately predicting the uncertain future. In the context of autonomous driving, deep neural …
accurately predicting the uncertain future. In the context of autonomous driving, deep neural …
Online parameter estimation for human driver behavior prediction
RP Bhattacharyya, R Senanayake… - 2020 American …, 2020 - ieeexplore.ieee.org
Driver models are invaluable for planning in autonomous vehicles as well as validating their
safety in simulation. Highly parameterized black-box driver models are very expressive, and …
safety in simulation. Highly parameterized black-box driver models are very expressive, and …
RuleFuser: Injecting Rules in Evidential Networks for Robust Out-of-Distribution Trajectory Prediction
Modern neural trajectory predictors in autonomous driving are developed using imitation
learning (IL) from driving logs. Although IL benefits from its ability to glean nuanced and …
learning (IL) from driving logs. Although IL benefits from its ability to glean nuanced and …
Multi-fidelity recursive behavior prediction
M Jain, K Brown, AK Sadek - arXiv preprint arXiv:1901.01831, 2018 - arxiv.org
Predicting the behavior of surrounding vehicles is a critical problem in automated driving.
We present a novel game theoretic behavior prediction model that achieves state of the art …
We present a novel game theoretic behavior prediction model that achieves state of the art …
Vehicle trajectory prediction using generative adversarial network with temporal logic syntax tree features
In this work, we propose a novel approach for integrating rules into traffic agent trajectory
prediction. Consideration of rules is important for understanding how people behave-yet, it …
prediction. Consideration of rules is important for understanding how people behave-yet, it …
Transferable and adaptable driving behavior prediction
While autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient, transferable, and …
driving, humans have long mastered the essence of driving with efficient, transferable, and …
Generic prediction architecture considering both rational and irrational driving behaviors
Accurately predicting future behaviors of surrounding vehicles is an essential capability for
autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others …
autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others …
Safe real-world autonomous driving by learning to predict and plan with a mixture of experts
The goal of autonomous vehicles is to navigate public roads safely and comfortably. To
enforce safety, traditional planning approaches rely on handcrafted rules to generate …
enforce safety, traditional planning approaches rely on handcrafted rules to generate …
Learning interaction-aware probabilistic driver behavior models from urban scenarios
Human drivers have complex and individual behavior characteristics which describe how
they act in a specific situation. Accurate behavior models are essential for many applications …
they act in a specific situation. Accurate behavior models are essential for many applications …
Deepware: An open-source toolkit for developing and evaluating learning-based and model-based autonomous driving models
In recent decades, many learning-based autonomous driving systems have been proposed,
and researchers have also created toolkits for developing these systems. These toolkits …
and researchers have also created toolkits for developing these systems. These toolkits …