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Bogdan Trasnea

2 accepted papers

2021

ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles

RA-L 2021

A key component in autonomous driving is the ability of the self-driving car to understand, track and predict the dynamics of the surrounding environment. Although there is significant work in the area of object detection, tracking and observations prediction, there is no prior work demonstrating th

Cited by 9SourceScholar
2019

NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles

RA-L 2019

Autonomous vehicles are controlled today either based on sequences of decoupled perception-planning-action operations, either based on End2End or deep reinforcement learning (DRL) systems. Current deep learning solutions for autonomous driving are subject to several limitations (e.g., they estimate

Cited by 40SourceScholar