Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hyb...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 24; číslo 7; s. 2097
Hlavní autori:	Rosero, Luis Alberto, Gomes, Iago Pachêco, da Silva, Júnior Anderson Rodrigues, Przewodowski, Carlos André, Wolf, Denis Fernando, Osório, Fernando Santos
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 25.03.2024 MDPI
Predmet:	Algorithms Artificial Intelligence autonomous driving CARLA simulator Computer Science Computer Vision and Pattern Recognition Decision making Decision theory Decomposition Deep learning end-to-end Engineering Sciences hybrid architecture Machine Learning Methods Modeling and Simulation modular Neural networks path planning Propagation Robotics Sensors Signal and Image processing Software Vehicles New Jersey end-to-end hybrid architecture modular path planning CARLA simulator autonomous driving CANBus Radar Untrasonic Autoware Autoware Modular Sensing GNSS IMU CARLA simulator GNSS Control Guardian HD-Map Localization V2I Modular Perception HMI GNSS Prediction LiDAR Map Radar IMU autonomous driving hybrid architecture modular end-to-end path planning CARLA simulator GNSS Perception Cameras V2V Planning Stereo Camera V2X Ultrasonic
ISSN:	1424-8220, 1424-8220
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