Deep Metadata Fusion for Traffic Light to Lane Assignment
نویسندگان
چکیده
منابع مشابه
Dynamic Traffic Assignment for Automated Highway Systems: A Two-lane Highway with Speed Constancy
Dynamic traffic assignment through analytical modeling and optimization has been widely accepted by the IVHS R&D community as a promising traffic control tool for understanding and relieving traffic congestion on conventional highways and city streets. Due to the completely controlled nature of AHS traffic, dynamic assignment of AHS traffic is even more promising. One added dimension of complex...
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Imagine that you are driving on the highway at 70 mph and trying to figure out which lane should follow to exit in front. The precision of the current GPS cannot tell on which lane you are on, so the instruction given by the GPS is just as simple as ”keep right” resulting in the driver’s panic caused by searching from multiple signs. However, if we have a smart camera mounted inside the vehicle...
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Traffic light timing optimization is still an active line of research despite the wealth of scientific literature on the topic, and the problem remains unsolved for any non-toy scenario. One of the key issues with traffic light optimization is the large scale of the input information that is available for the controlling agent, namely all the traffic data that is continually sampled by the traf...
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Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals’ duration, existing works either split the traffic signal into equal duration or extra...
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Driving behavior models for lane-changing and acceleration form an integral component of microscopic traffic simulators and determine its value in evaluation of different traffic management strategies. The state-of-art model for lane changing adopts a two-level framework: the first level involves a latent or unobserved choice of a target lane; the second level models the acceptance of adjacent ...
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ژورنال
عنوان ژورنال: IEEE Robotics and Automation Letters
سال: 2019
ISSN: 2377-3766,2377-3774
DOI: 10.1109/lra.2019.2893446