{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:58:37Z","timestamp":1771275517176,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["Campus for Research Excellence And Technological Enterprise"],"award-info":[{"award-number":["Campus for Research Excellence And Technological Enterprise"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21041039","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T20:31:51Z","timestamp":1612384311000},"page":"1039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Multi-Task End-to-End Self-Driving Architecture for CAV Platoons"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4351-4493","authenticated-orcid":false,"given":"Sebastian","family":"Huch","sequence":"first","affiliation":[{"name":"TUMCREATE, 1 CREATE Way, #10-02 CREATE Tower, Singapore 138602, Singapore"},{"name":"Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8160-5815","authenticated-orcid":false,"given":"Aybike","family":"Ongel","sequence":"additional","affiliation":[{"name":"TUMCREATE, 1 CREATE Way, #10-02 CREATE Tower, Singapore 138602, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-2849","authenticated-orcid":false,"given":"Johannes","family":"Betz","sequence":"additional","affiliation":[{"name":"Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, 220 South 33rd Street Philadelphia, Philadelphia, PA 19104, USA"}]},{"given":"Markus","family":"Lienkamp","sequence":"additional","affiliation":[{"name":"TUMCREATE, 1 CREATE Way, #10-02 CREATE Tower, Singapore 138602, Singapore"},{"name":"Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"ref_1","unstructured":"SAE (2018). 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