Processing Traffic Jam in Al-Sadrain Intersection in the Holy City of Najaf

Modern metropolitan cities worldwide suffer from heavy congestion due to high daily commutes for various purposes. Intersections are the most congested component of the network and represent the sites of accidents. At the same time, the intersection (Al-Sadrien) in Al- Najaf Al-Ashraf city is regarded as one of the major important intersections. Typically, this intersection increases the volume of traffic during rush hours, which raises traffic congestion, Therefor, the problem with this article is that unclear how the distribution of the surrounding areas affects traffic accidents and junction congestion. Our hypothesis to solve the problem is that an important relationship must be clear between the intersection and its surrounding areas. In this study, analysis the current service of intersection as field survey adopting the analytical approach using simulation by HCS2010 and VISSIM software, based on GIS that showed: the eastern approach (coming from Al- Kufa) was (F) (3.18.97), the result of the western approach (coming from Najaf) (F) (3.45.49), and the result of the northern approach (coming from the College of Administration and Economics) (D) (1.38). 83) and the result of the southern approach (coming from Al- Rawan Street) (E) (1.14.13). When the times of delay to the level of service intersection, as adopted by the capacity of roads on the program (HCS 2010), amounted to more than (800) seconds/vehicle at peak times. So, the classification of service at this intersection is in level (E). Through the traffic intersection analysis, recommendations and proposals must be taken before any decisions regarding land use , which has effects on the city

TrafficPredict: Trajectory Prediction for Heterogeneous 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.Comment: Accepted by AAAI(Oral) 201

Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0.18m on a prediction horizon of 5s.Comment: the article has been accepted for publication during the 23rd IEEE Intelligent Transportation Systems Conference (ITSC), 7 pages, 6 figures, 1 tabl

Detección de carril vehicular utilizando el YOLO como sensor

Se desarrolla un procedimiento encargado de detectar vehículos circulando por carriles en una avenida. Se utiliza el programa YOLO como sensor de objetos. Mediante la integración del YOLO y un programa de visión artificial se facilita el análisis de información en un área dentro de un fotograma. Se dividió la avenida en tres carriles denominados: carril izquierdo (CI), carril central (CC) y carril derecho (CD). Se establece en el fotograma un área rectangular de base y altura conocida. Seguidamente, el YOLO detecta todos los objetos en el fotograma. La información recolectada se agrupa en una lista junto a sus coordenadas. Mediante un algoritmo, se analiza dicha lista para detectar los vehículos pertenecientes al área de trabajo. Los límites de cada carril CI, CC y CD están comprendidos sobre la base del área rectangular. Las posiciones, pertenecientes a la lista de automóviles, se comparan con los límites de los carriles CC, CI y CD. El resultado de las comparaciones permite reconocer vehículos pertenecientes a los carriles CC, CI y CD dentro de una región del fotograma