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

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...

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Vydané v:	IEEE robotics and automation letters Ročník 4; číslo 4; s. 3441 - 3448
Hlavní autori:	Grigorescu, Sorin Mihai, Trasnea, Bogdan, Marina, Liviu, Vasilcoi, Andrei, Cocias, Tiberiu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Actuators Artificial neural networks Autonomous vehicles Back propagation Estimation Fitness Genetic algorithms Machine learning Mapping Motion control Neural networks Perception Pipelines Sequences Task analysis Training Trajectories Trajectory
ISSN:	2377-3766, 2377-3766
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