2,635 research outputs found
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning
Deep Reinforcement Learning has proved to be able to solve many control tasks
in different fields, but the behavior of these systems is not always as
expected when deployed in real-world scenarios. This is mainly due to the lack
of domain adaptation between simulated and real-world data together with the
absence of distinction between train and test datasets. In this work, we
investigate these problems in the autonomous driving field, especially for a
maneuver planning module for roundabout insertions. In particular, we present a
system based on multiple environments in which agents are trained
simultaneously, evaluating the behavior of the model in different scenarios.
Finally, we analyze techniques aimed at reducing the gap between simulated and
real-world data showing that this increased the generalization capabilities of
the system both on unseen and real-world scenarios.Comment: Intelligent Vehicle Symposium 2020 (IV2020
Machine learning, social learning and the governance of self-driving cars
Self-driving cars, a quintessentially ‘smart’ technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking ‘Who is learning, what are they learning and how are they learning?’ Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. ‘Self-driving’ or ‘autonomous’ cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning
- …