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Visual Autonomous Road Following by Symbiotic Online Learning
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0002-6096-3648
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
2016 (Engelska)Ingår i: Intelligent Vehicles Symposium (IV), 2016 IEEE, 2016, s. 136-143Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Recent years have shown great progress in driving assistance systems, approaching autonomous driving step by step. Many approaches rely on lane markers however, which limits the system to larger paved roads and poses problems during winter. In this work we explore an alternative approach to visual road following based on online learning. The system learns the current visual appearance of the road while the vehicle is operated by a human. When driving onto a new type of road, the human driver will drive for a minute while the system learns. After training, the human driver can let go of the controls. The present work proposes a novel approach to online perception-action learning for the specific problem of road following, which makes interchangeably use of supervised learning (by demonstration), instantaneous reinforcement learning, and unsupervised learning (self-reinforcement learning). The proposed method, symbiotic online learning of associations and regression (SOLAR), extends previous work on qHebb-learning in three ways: priors are introduced to enforce mode selection and to drive learning towards particular goals, the qHebb-learning methods is complemented with a reinforcement variant, and a self-assessment method based on predictive coding is proposed. The SOLAR algorithm is compared to qHebb-learning and deep learning for the task of road following, implemented on a model RC-car. The system demonstrates an ability to learn to follow paved and gravel roads outdoors. Further, the system is evaluated in a controlled indoor environment which provides quantifiable results. The experiments show that the SOLAR algorithm results in autonomous capabilities that go beyond those of existing methods with respect to speed, accuracy, and functionality. 

Ort, förlag, år, upplaga, sidor
2016. s. 136-143
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:liu:diva-128264DOI: 10.1109/IVS.2016.7535377ISI: 000390845600025ISBN: 978-1-5090-1821-5 (digital)ISBN: 978-1-5090-1822-2 (tryckt)OAI: oai:DiVA.org:liu-128264DiVA, id: diva2:947322
Konferens
2016 IEEE Intelligent Vehicles Symposium (IV), 19-22 June, Gothenburg, Sweden
Tillgänglig från: 2016-07-07 Skapad: 2016-05-24 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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Öfjäll, KristofferFelsberg, MichaelRobinson, Andreas
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DatorseendeTekniska fakultetenCentrum för medicinsk bildvetenskap och visualisering, CMIV
Datorseende och robotik (autonoma system)

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