19,333 research outputs found
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.Comment: 8 pages, 6 figure
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Scenario-based testing for the safety validation of highly automated vehicles
is a promising approach that is being examined in research and industry. This
approach heavily relies on data from real-world scenarios to derive the
necessary scenario information for testing. Measurement data should be
collected at a reasonable effort, contain naturalistic behavior of road users
and include all data relevant for a description of the identified scenarios in
sufficient quality. However, the current measurement methods fail to meet at
least one of the requirements. Thus, we propose a novel method to measure data
from an aerial perspective for scenario-based validation fulfilling the
mentioned requirements. Furthermore, we provide a large-scale naturalistic
vehicle trajectory dataset from German highways called highD. We evaluate the
data in terms of quantity, variety and contained scenarios. Our dataset
consists of 16.5 hours of measurements from six locations with 110 000
vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane
changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems
(ITSC) 201
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