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Syst."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>As a first, the paper proposes modelling and learning of specific behaviors for dynamic obstacle avoidance in end-to-end motion planning. In the literature many end-to-end methods have been used in simulators to drive a car and to apply the learnt strategies to avoid the obstacles using the lane changing, following the vehicle as per the traffic rules, driving in-between the lane boundaries, and many more behaviors. The proposed method is designed to avoid obstacles in the scenarios where a dynamic obstacle is headed directly towards the robot from different directions. To avoid the critical encounter of the dynamic obstacles, we trained a novel deep neural network (DNN) with two specific behavioral obstacle avoidance strategies, namely<jats:italic>\u201chead-on collision avoidance\u201d<\/jats:italic>and<jats:italic>\u201cstop and move\u201d<\/jats:italic>. These two strategies of obstacle avoidance come from the human behavior of obstacle avoidance. Looking at the current frame only, for a very similar visual display of the scenario, the two strategies have contrasting outputs and overall outcomes that makes learning very difficult. A random data recording over general simulations is unlikely to record the corner cases of both behaviors that rarely occur, and a behavior-specific training used in this paper intensifies the same cases for a better learning of the robot in such corner cases. We calculate the intention of the obstacle, whether it will move or not. This proposed method is compared with three state-of-the-art methods of motion planning, namely Timed-Elastic Band, Dynamic Window Approach and Nonlinear Probabilistic Velocity Obstacle. The proposed method beats all the state-of-the-art methods used for comparisons.<\/jats:p>","DOI":"10.1007\/s40747-022-00755-0","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T09:03:14Z","timestamp":1651568594000},"page":"5065-5086","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["End-to-end human inspired learning based system for dynamic obstacle avoidance"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0339-5805","authenticated-orcid":false,"given":"S. M. 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The authors declare that the work described has not been published previously, that is not under the consideration for publication elsewhere, that its publication is approved by all the authors and tacitly or explicitly by the responsible authorities where the work has been carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without written consent of copyright- holder. The authors don\u2019t have any competing interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}