Energy efficient driving in dynamic environment

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 67599

Context embedded energy efficient driving

For almost a decade, energy efficient driving occupies the attention of researchers and engineers. Still the consideration of overtaking in eco-driving didn’t receive a lot of attention yet. In this work energy efficient driving of (semi)autonomous electric vehicles operating in environment with other traffic participants is studied. Neglecting the constraints imposed from the surrounding traffic, when generating an energy optimal speed trajectory may lead to trajectories which are not attainable in real driving situation which may eventually lead to bigger energy consumption and low driver acceptance. An existing approach, which considers other traffic participants and optimizes the overtaking problem, modifies a previously generated unconstrained trajectory to satisfy constraints arisen from surrounding traffic. In contrast to this, the proposed approach incorporates a leading vehicle’s motion as constraint in original optimal control problem. By this approach the generated trajectory is globally optimal. Besides that, this approach considers overtaking decision making and leaves possibility to not overtake at all

Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles

To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low surface, the vehicle should utilize drifting. Hence many authors have devised rules to split circuits and employ drifting on some segments. These rules are suboptimal and do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the question "When to go into which mode and how to drive in it?" remains unanswered. To choose the suitable mode (discrete decision), the algorithm needs information about the feasibility of the continuous motion in that mode. This makes it a class of Task and Motion Planning (TAMP) problems, which are known to be hard to solve optimally in real-time. In the AI planning community, search methods are commonly used. However, they cannot be directly applied to TAMP problems due to the continuous component. Here, we present a search-based method that effectively solves this problem and efficiently searches in a highly dimensional state space with nonlinear and unstable dynamics. The space of the possible trajectories is explored by sampling different combinations of motion primitives guided by the search. Our approach allows to use multiple locally approximated models to generate motion primitives (e.g., learned models of drifting) and effectively simplify the problem without losing accuracy. The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left). Our code is available at https://git.io/JenvBComment: Accepted to the journal Engineering Applications of Artificial Intelligence; 19 pages, 18 figures, code: https://git.io/JenvB. arXiv admin note: text overlap with arXiv:1907.0782

Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals

Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide a short overview of global trends, applications in industry and selected use-cases from our international experience and work in industry and academia. The goal is to present global and regional positive practices and provide an informed opinion on the realistic goals and opportunities for positioning B&H on the global AI scene

Interactive Imitation Learning in Robotics: A Survey

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research

Energy Efficient Autopilot: Energy efficient driving in dynamic environment

For almost a decade, energy efficient driving occupies the attention of researchers and engineers. Still the consideration of overtaking in eco-driving didn’t receive a lot of attention yet. In this work energy efficient driving of (semi)autonomous electric vehicles operating in environment with other traffic participants is studied. Neglecting the constraints imposed from the surrounding traffic, when generating an energy optimal speed trajectory may lead to trajectories which are not attainable in real driving situation which may eventually lead to bigger energy consumption and low driver acceptance. An existing approach, which considers other traffic participants and optimizes the overtaking problem, modifies a previously generated unconstrained trajectory to satisfy constraints arisen from surrounding traffic. In contrast to this, the proposed approach incorporates a leading vehicle’s motion as constraint in original optimal control problem. By this approach the generated trajectory is globally optimal. Besides that, this approach considers overtaking decision making and leaves possibility to not overtake at all