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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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 24; H. 7; S. 2097
Hauptverfasser: Rosero, Luis Alberto, Gomes, Iago Pachêco, da Silva, Júnior Anderson Rodrigues, Przewodowski, Carlos André, Wolf, Denis Fernando, Osório, Fernando Santos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 25.03.2024
MDPI
Schlagworte:
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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Zusammenfassung: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 hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved first and second place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation. We achieved a driving score of 41.56 and the highest route completion of 86.03 in the MAP track of the CARLA Challenge leaderboard 1, and driving scores of 35.36 and 1.23 in the CARLA Challenge SENSOR track with route completions of 85.01 and 9.55, for, respectively, leaderboard 1 and 2. The results of leaderboard 2 raised the hybrid architecture to the first position, winning the edition of the 2023 CARLA Autonomous Driving Competition.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24072097