3 papers
cs.RO2019
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)…
cs.CV2019
Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding
Liviu Marina, Bogdan Trasnea, Cocias Tiberiu +3
Grid maps obtained from fused sensory information are nowadays among the most popular approaches for motion planning for autonomous driving cars. In this paper, we introduce Deep G…
cs.RO2019
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…