20 citations · 58 across the 10 of their papers we have counts for
5 papers · 1 filter
ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles
Cosmin Ginerica, Mihai Zaha, Florin Gogianu +3
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 signifi…
OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots
Bogdan Trasnea, Cosmin Ginerica, Mihai Zaha +3
Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan t…
LVD-NMPC: A Learning-based Vision Dynamics Approach to Nonlinear Model Predictive Control for Autonomous Vehicles
Sorin Grigorescu, Cosmin Ginerica, Mihai Zaha +2
In this paper, we introduce a learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles, coined LVD-NMPC. LVD-NMPC uses an a-priori proc…
NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles
Sorin Grigorescu, Bogdan Trasnea, Liviu Marina +2
Autonomous vehicles are controlled today either based on sequences of decoupled perception-planning-action operations, either based on End2End or Deep Reinforcement Learning (DRL)…
GridSim: A Vehicle Kinematics Engine for Deep Neuroevolutionary Control in Autonomous Driving
Bogdan Trasnea, Andrei Vasilcoi, Claudiu Pozna +1
Current state of the art solutions in the control of an autonomous vehicle mainly use supervised end-to-end learning, or decoupled perception, planning and action pipelines. Anothe…