On the Verification of Cooperating Traffic Agents

2004 ◽  
pp. 77-110 ◽  
Author(s):  
Werner Damm ◽  
Hardi Hungar ◽  
Ernst-Rüdiger Olderog
Keyword(s):  
Traffic Agents

scholarly journals TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

2019 ◽  
Vol 33 ◽  
pp. 6120-6127 ◽  
Author(s):  
Yuexin Ma ◽  
Xinge Zhu ◽  
Sibo Zhang ◽  
Ruigang Yang ◽  
Wenping Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Large City ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Prediction Algorithm ◽  
Trajectory Prediction ◽  
Long Short Term Memory ◽  
Traffic Agents

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.

scholarly journals Traffic Agents for Improving QoS in Mixed Infrastructure and Ad Hoc Modes Wireless LAN

2006 ◽  
Vol 2006 (1) ◽  
Author(s):  
Yang Yang ◽  
Hai-Feng Yuan ◽  
Hsiao-Hwa Chen ◽  
Wen-Bing Yao ◽  
Yong-Hua Song
Keyword(s):  
Wireless Lan ◽  
Ad Hoc ◽  
Traffic Agents

scholarly journals BASTA: BDI-based architecture of simulated traffic agents

2020 ◽  
Vol 4 (4) ◽  
pp. 440-460
Author(s):  
Inga Rüb ◽  
Barbara Dunin-Kȩplicz
Keyword(s):  
Traffic Agents

scholarly journals Prevalence and consequences of sleep disorders among traffic agents: A case study

2015 ◽  
Vol 17 (54) ◽  
pp. 207-211
Author(s):  
Aldo Pacheco Ferreira
Keyword(s):  
Sleep Disorders ◽  
Traffic Agents

User experience and operational feasibility of four point-of-collection oral fluid drug-testing devices according to Brazilian traffic agents

2019 ◽  
Vol 20 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Flavio Pechansky ◽  
Juliana Nichterwitz Scherer ◽  
Jaqueline B. Schuch ◽  
Vinícius Roglio ◽  
Yeger Moreschi Telles ◽  
...  
Keyword(s):  
User Experience ◽  
Drug Testing ◽  
Oral Fluid ◽  
Traffic Agents

scholarly journals Comparative Analysis of Variants of Token Bucket Traffic Meter Algorithms for QoS Router using UDP as Traffic Agent

2018 ◽  
pp. 91-104
Author(s):  
Oyetunji M.O. ◽  
Emuoyinbofarhe O.J. ◽  
Oladosu J.B ◽  
Oladeji F.O.
Keyword(s):  
Comparative Analysis ◽  
Loss Rate ◽  
Service Level Agreement ◽  
Network Models ◽  
Packet Delay ◽  
Service Level ◽  
Internet Service ◽  
Anova Model ◽  
Token Bucket ◽  
Traffic Agents

Traffic meter algorithms serve as a means of examining traffic stream’s conformance with service level agreement between customers (traffic sources) and Internet Service provider at the edge router of a differentiated service domain for proper quality of service admission control. This paper presented comparative analysis of variants of token bucket meter algorithms for QoS router using user datagram protocol (UDP) as traffic agents and exponential ON/OFF as traffic generator. The research adopted simulation technique to carry out the design of network models or topologies using the same parameter setting to implement the algorithm of token bucket variants of traffic meter. The following metrics were used for the evaluation: throughput, fairness rate, loss rate and one-way packet delay. The evaluated results were ranked and further subjected to 2-way analysis of variance (ANOVA) model to indicate the significant differences among the traffic meter algorithms. Based on ranking system, TRTCM was ranked first in terms of throughput (with 67117) and fairness rate (with 0.2586) and TBM was ranked first in terms of loss rate (with 74.003) and one-way packet delay (with 0.09304). The 2-way ANOVA model showed the significant differences among the traffic meter algorithms considered for the simulation.

scholarly journals Assessment of Traffic Noise Pollution in Traffic Agents

Health Care ◽  
2013 ◽  
Vol 1 (3) ◽  
pp. 75
Author(s):  
Maria do Socorro Oliveira Soares ◽  
Karla Geovanna Moraes Crispim ◽  
Aldo Pacheco Ferreira
Keyword(s):  
Traffic Noise ◽  
Noise Pollution ◽  
Traffic Agents
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