5,944 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
End-to-end Driving via Conditional Imitation Learning
Deep networks trained on demonstrations of human driving have learned to
follow roads and avoid obstacles. However, driving policies trained via
imitation learning cannot be controlled at test time. A vehicle trained
end-to-end to imitate an expert cannot be guided to take a specific turn at an
upcoming intersection. This limits the utility of such systems. We propose to
condition imitation learning on high-level command input. At test time, the
learned driving policy functions as a chauffeur that handles sensorimotor
coordination but continues to respond to navigational commands. We evaluate
different architectures for conditional imitation learning in vision-based
driving. We conduct experiments in realistic three-dimensional simulations of
urban driving and on a 1/5 scale robotic truck that is trained to drive in a
residential area. Both systems drive based on visual input yet remain
responsive to high-level navigational commands. The supplementary video can be
viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation
(ICRA), 201
DoShiCo Challenge: Domain Shift in Control Prediction
Training deep neural network policies end-to-end for real-world applications
so far requires big demonstration datasets in the real world or big sets
consisting of a large variety of realistic and closely related 3D CAD models.
These real or virtual data should, moreover, have very similar characteristics
to the conditions expected at test time. These stringent requirements and the
time consuming data collection processes that they entail, are currently the
most important impediment that keeps deep reinforcement learning from being
deployed in real-world applications. Therefore, in this work we advocate an
alternative approach, where instead of avoiding any domain shift by carefully
selecting the training data, the goal is to learn a policy that can cope with
it. To this end, we propose the DoShiCo challenge: to train a model in very
basic synthetic environments, far from realistic, in a way that it can be
applied in more realistic environments as well as take the control decisions on
real-world data. In particular, we focus on the task of collision avoidance for
drones. We created a set of simulated environments that can be used as
benchmark and implemented a baseline method, exploiting depth prediction as an
auxiliary task to help overcome the domain shift. Even though the policy is
trained in very basic environments, it can learn to fly without collisions in a
very different realistic simulated environment. Of course several benchmarks
for reinforcement learning already exist - but they never include a large
domain shift. On the other hand, several benchmarks in computer vision focus on
the domain shift, but they take the form of a static datasets instead of
simulated environments. In this work we claim that it is crucial to take the
two challenges together in one benchmark.Comment: Published at SIMPAR 2018. Please visit the paper webpage for more
information, a movie and code for reproducing results:
https://kkelchte.github.io/doshic
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