37,547 research outputs found
COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning
Platooning and coordination are two implementation strategies that are
frequently proposed for traffic control of connected and autonomous vehicles
(CAVs) at signal-free intersections instead of using conventional traffic
signals. However, few studies have attempted to integrate both strategies to
better facilitate the CAV control at signal-free intersections. To this end,
this study proposes a hierarchical control model, named COOR-PLT, to coordinate
adaptive CAV platoons at a signal-free intersection based on deep reinforcement
learning (DRL). COOR-PLT has a two-layer framework. The first layer uses a
centralized control strategy to form adaptive platoons. The optimal size of
each platoon is determined by considering multiple objectives (i.e.,
efficiency, fairness and energy saving). The second layer employs a
decentralized control strategy to coordinate multiple platoons passing through
the intersection. Each platoon is labeled with coordinated status or
independent status, upon which its passing priority is determined. As an
efficient DRL algorithm, Deep Q-network (DQN) is adopted to determine platoon
sizes and passing priorities respectively in the two layers. The model is
validated and examined on the simulator Simulation of Urban Mobility (SUMO).
The simulation results demonstrate that the model is able to: (1) achieve
satisfactory convergence performances; (2) adaptively determine platoon size in
response to varying traffic conditions; and (3) completely avoid deadlocks at
the intersection. By comparison with other control methods, the model manifests
its superiority of adopting adaptive platooning and DRL-based coordination
strategies. Also, the model outperforms several state-of-the-art methods on
reducing travel time and fuel consumption in different traffic conditions.Comment: This paper has been submitted to Transportation Research Part C:
Emerging Technologies and is currently under revie
Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics
Developmental robotics is an emerging field located
at the intersection of developmental psychology
and robotics, that has lately attracted
quite some attention. This paper gives a survey of
a variety of research projects dealing with or inspired
by developmental issues, and outlines possible
future directions
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
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