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Multi-Task End-to-End Self-Driving Architecture for CAV Platoons

TUMCREATE, 1 CREATE Way, #10-02 CREATE Tower, Singapore 138602, Singapore
Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Munich, Germany
Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, 220 South 33rd Street Philadelphia, Philadelphia, PA 19104, USA
Author to whom correspondence should be addressed.
Academic Editor: Chao Huang
Sensors 2021, 21(4), 1039;
Received: 21 December 2020 / Revised: 29 January 2021 / Accepted: 31 January 2021 / Published: 3 February 2021
(This article belongs to the Special Issue Advanced Sensing and Control for Connected and Automated Vehicles)
Connected and autonomous vehicles (CAVs) could reduce emissions, increase road safety, and enhance ride comfort. Multiple CAVs can form a CAV platoon with a close inter-vehicle distance, which can further improve energy efficiency, save space, and reduce travel time. To date, there have been few detailed studies of self-driving algorithms for CAV platoons in urban areas. In this paper, we therefore propose a self-driving architecture combining the sensing, planning, and control for CAV platoons in an end-to-end fashion. Our multi-task model can switch between two tasks to drive either the leading or following vehicle in the platoon. The architecture is based on an end-to-end deep learning approach and predicts the control commands, i.e., steering and throttle/brake, with a single neural network. The inputs for this network are images from a front-facing camera, enhanced by information transmitted via vehicle-to-vehicle (V2V) communication. The model is trained with data captured in a simulated urban environment with dynamic traffic. We compare our approach with different concepts used in the state-of-the-art end-to-end self-driving research, such as the implementation of recurrent neural networks or transfer learning. Experiments in the simulation were conducted to test the model in different urban environments. A CAV platoon consisting of two vehicles, each controlled by an instance of the network, completed on average 67% of the predefined point-to-point routes in the training environment and 40% in a never-seen-before environment. Using V2V communication, our approach eliminates casual confusion for the following vehicle, which is a known limitation of end-to-end self-driving. View Full-Text
Keywords: connected and autonomous vehicles; artificial neural networks; end-to-end learning; multi-task learning; urban vehicle platooning; simulation connected and autonomous vehicles; artificial neural networks; end-to-end learning; multi-task learning; urban vehicle platooning; simulation
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MDPI and ACS Style

Huch, S.; Ongel, A.; Betz, J.; Lienkamp, M. Multi-Task End-to-End Self-Driving Architecture for CAV Platoons. Sensors 2021, 21, 1039.

AMA Style

Huch S, Ongel A, Betz J, Lienkamp M. Multi-Task End-to-End Self-Driving Architecture for CAV Platoons. Sensors. 2021; 21(4):1039.

Chicago/Turabian Style

Huch, Sebastian, Aybike Ongel, Johannes Betz, and Markus Lienkamp. 2021. "Multi-Task End-to-End Self-Driving Architecture for CAV Platoons" Sensors 21, no. 4: 1039.

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