4,276 research outputs found
An Overview about Emerging Technologies of Autonomous Driving
Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. This
paper gives an overview about technical aspects of autonomous driving
technologies and open problems. We investigate the major fields of self-driving
systems, such as perception, mapping and localization, prediction, planning and
control, simulation, V2X and safety etc. Especially we elaborate on all these
issues in a framework of data closed loop, a popular platform to solve the long
tailed autonomous driving problems
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
Connected automated driving has the potential to significantly improve urban
traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative
behavior planning can be employed to jointly optimize the motion of multiple
vehicles. Most existing approaches to automatic intersection management,
however, only consider fully automated traffic. In practice, mixed traffic,
i.e., the simultaneous road usage by automated and human-driven vehicles, will
be prevalent. The present work proposes to leverage reinforcement learning and
a graph-based scene representation for cooperative multi-agent planning. We
build upon our previous works that showed the applicability of such machine
learning methods to fully automated traffic. The scene representation is
extended for mixed traffic and considers uncertainty in the human drivers'
intentions. In the simulation-based evaluation, we model measurement
uncertainties through noise processes that are tuned using real-world data. The
paper evaluates the proposed method against an enhanced first in - first out
scheme, our baseline for mixed traffic management. With increasing share of
automated vehicles, the learned planner significantly increases the vehicle
throughput and reduces the delay due to interaction. Non-automated vehicles
benefit virtually alike.Comment: 8 pages, 7 figures, 34th IEEE Intelligent Vehicles Symposium (IV),
updated to accepted versio
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