Visually-guided motion planning for autonomous driving from interactive demonstrations
نویسندگان
چکیده
The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current navigation systems mainly rely geometric information hard-coded rules induce safe compliant behaviors. Yet, unstructured urban scenarios these approaches can become costly suboptimal. In this paper, we introduce a motion planning framework consisting two components: data-driven policy that uses visual inputs human feedback generate driving behaviors (encoded by high-level decision variables), local trajectory optimization method executes (ensuring safety). particular, employ Interactive Imitation Learning jointly train the with planner, Model Predictive Controller (MPC), which results human-like Our approach is validated realistic simulated scenarios. Qualitative show similarity learned driving. Furthermore, performance substantially improved terms safety, i.e., number collisions, as compared prior frameworks, data-efficiency learning-based broadening operational domain MPC more • behavior optimization-based planners. Trajectory planners are safer socially-compliant when they resemble behavior. allows learn few hours teaching.
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*Centro de Investigaci on en Matem aticas, CIMAT, Jalisco s/n. Col. Valenciana, 36240 Guanajuato, Guanajuato, M exico Departamento de Matem aticas, Universidad de Guanajuato, Jalisco s/n. Col. Valenciana, 36240 Guanajuato, Guanajuato, M exico Robotics and Advanced Manufacturing Group, Centro de Investigaci on y de Estudios Avanzados del IPN, CINVESTAV, Saltillo, Coah, M exico §LAAS-CNRS, Labora...
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ژورنال
عنوان ژورنال: Engineering Applications of Artificial Intelligence
سال: 2022
ISSN: ['1873-6769', '0952-1976']
DOI: https://doi.org/10.1016/j.engappai.2022.105277