Abstract
Autonomous and connected cars are almost here, and soon will be an everyday reality. Driver desired comfort, road conditions, travel dynamics and communication requirements between vehicles have to be considered. Simulation can help us to find how to improve road safety and comfort in traveling. Traffic flow models have been widely used in recent years to improve traffic management through understanding how current laws, with human drivers, should change in this new environment. Early attempts to driving modelling were restricted to the macroscopic level, mimicking continuous physical patterns, particularly waves. However, extensive improvements in technology have allowed the tracking of individual drivers in more detail. In this paper, the Intelligent Driver Model (IDM) is used to examine traffic flow behavior at a vehicle level with emphasis on the relation to the preceding vehicle, similarly as it is done by the Adaptive Cruise Control (ACC) systems nowadays. This traffic model has been modified to simulate vehicles at low speed and the interactions with their preceding vehicles; more specifically, in traffic congestion situations. This traffic jam scenario has been analyzed with a developed simulation tool. The results are encouraging, as they prove that automatic car speed control can potentially improve road safety and reduce driver stress.
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Echeto, J., Romana, M.G., Santos, M. (2021). Swarm Modelling Considering Autonomous Vehicles for Traffic Jam Assist Simulation. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_41
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DOI: https://doi.org/10.1007/978-3-030-57802-2_41
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