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Trajectory and behavior prediction for autonomous vehicles in urban traffic

Grant number: 19/27301-7
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): July 01, 2020
Effective date (End): May 14, 2024
Field of knowledge:Engineering - Electrical Engineering
Principal Investigator:Denis Fernando Wolf
Grantee:Iago Pachêco Gomes
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment, AP.TEM
Associated scholarship(s):22/04473-0 - Adaptive interaction-aware trajectory prediction for autonomous vehicles in urban scenarios, BE.EP.DR


Autonomous vehicles should transform the urban transport scenario, increasing its efficiency, making it more accessible and safer, reducing the environmental impact, among other benefits. To do so, they are composed of a set of hardware and algorithms, which allow them to understand the external environment, their own state and interact with other traffic agents. To navigate safely, vehicles have algorithms for detecting, classifying and deflecting obstacles. However, because of the dynamism of urban traffic, just obstacle detection is not sufficient to ensure the safety of passengers and other traffic agents. Thus, the tracking, behavior prediction, and trajectory prediction of these agents are essential sources of information that enable decision-making and planning algorithms to consider likely scenarios regarding the position of these other agents, anticipating possible collisions or dangerous situations. Thus, the objective of this project is to investigate and develop techniques for predicting behavior and trajectories, which considers in addition to physical models of vehicle movement, road geometries, traffic rules and models of interaction between vehicles. Therefore, it is proposed to use dynamic bayesian networks that are able to model conditional relationships between variables and temporal dependence. In addition to Markov hidden models, to model the stage of execution of a maneuver or behavior, and recurrent neural networks, known as LSTM (Long Short-Term Memory), to predict the final trajectories. To validate the proposal, a public database and CARLA simulator will be used. Once validated, a real experiment will be performed using the autonomous vehicle CaRINA II, under development by the Mobile Robotics Laboratory (LRM) and the Intelligent Systems Laboratory (LASI) of the University of São Paulo - São Carlos. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
GOMES, IAGO PACHECO; WOLF, DENIS FERNANDO. Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 101, n. 1, . (19/27301-7)

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