I am a postdoctoral researcher in Automatic Control at UGE formely IFSTTAR. My recent research interests include Networked Control Systems (NCS), Cyberphysical Systems (CPS), Intelligent Transportation Systems (ITS) and Artificial Intelligence (AI). Previously, I did my PhD at INRIA/CNRS with the NeCS team developing Short-term Forecasting and estimation techniques for large scale traffic networks. Currently, I work within the LICIT team working on control techniques for connected and automated vehicles (CAV)s. In this team I develop traffic flow models and traffic simulation algorithms for truck platoon strategies.
I am also passioned about the Data Science community and the recent work developed around reproducible research. Fan of #rstats and python.
PhD in Automatic Control, 2018
Université Grenoble Alpes
MEng in Electronic Engineering, 2012
Pontifical Xavierian University
BSc in Electronic Engineering, 2008
Pontifical Xavierian University
Manipulation, DataViz, Modeling
Predictive, Non-linear, Gurobi, CVX
Hardware in the Loop / Control toolbox
Hardware Oriented Development
PyViz, Pandas, Keras
Taught in electronic engineering program. Associated Courses:
Program Assistant: Participated in program certification. Communication’s leader: Project ADDE SALEM
Truck platooning has a great potential to improve road safety, reduce emissions and increase transport efficiency. Significant advances …
SPEEDD developed a prototype for proactive event-driven decision-making: decisions were triggered by forecasting events-whether they …
Truck platooning has attracted substantial attention due to its pronounced benefits in saving energy and promising business model in freight transportation. However, one prominent challenge for the successful implementation of truck platooning is the safe and efficient interaction with surrounding traffic, especially at network discontinuities where mandatory lane changes may lead to the decoupling of truck platoons. This contribution puts forward an efficient method for splitting a platoon of vehicles near network merges. A model-based bi-level control strategy is proposed. A supervisory tactical strategy based on a first-order car-following model with bounded acceleration is designed to maximize the flow at merge discontinuities. The decisions taken at this level include optimal vehicle order after the merge, new equilibrium gaps of automated trucks at the merging point, and anticipation horizon that the platoon members start to track the new equilibrium gaps. The lower-level operational layer uses a third-order longitudinal dynamics model to compute the optimal truck accelerations so that new equilibrium gaps have been created when merging vehicles start to change lane and the transient maneuvers are efficient, safe and comfortable. The tactical decisions are derived from an analytic car-following model and the operational accelerations are controlled via model predictive control with guaranteed stability. Simulation experiments are provided in order to test the feasibility and demonstrate the performance and robustness of the proposed strategy.
This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.