Multistage Stochastic Model Predictive Control for Urban Automated Driving
Tommaso Benciolini, Tim Brüdigam, Marion Leibold
备注：
This work has been accepted to the IEEE 2021 International Conference on Intelligent Transportation Systems. The published version may be found at https://doi.org/10.1109/ITSC48978.2021.9564572
Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result in overly conservative motion planning. Hence, we propose to use Stochastic Model Predictive Control for vehicle control in urban driving, allowing to efficiently plan the vehicle trajectory, while maintaining the risk probability sufficiently low. For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately. A high-level layer takes advantage of a long prediction horizon and of an abstract model to plan the optimal maneuver, and a lower level is in charge of executing the selected maneuver by properly planning the vehicle's trajectory. Numerical simulations are included, showing the potential of our proposal.
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Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization
HERON - Hellenic Robotics Center of Excellence（希腊机器人 excellence 中心）；Robotics Institute（机器人研究所）；School of Electrical & Computer Engineering（电气与计算机工程学院）；National Technical University of Athens（雅典技术大学）
We study operations of a battery energy storage system under a baseline-based demand response (DR) program with an uncertain schedule of DR events. Baseline-based DR programs may provide undesired incentives to inflate baseline consumption in non-event days, in order to increase "apparent" DR reduction in event days and secure higher DR payments. Our goal is to identify and quantify such incentives. To understand customer decisions, we formulate the problem of determining hourly battery charging and discharge schedules to minimize expected net costs, defined as energy purchase costs minus ener…
Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios
Institute of Control Systems, Karlsruhe Institute for Technology（控制系统研究所，卡尔斯鲁厄技术学院）；Wuerzburg Institute for Traffic Sciences GmbH（魏玛交通科学研究所有限公司）；Faculty of Engineering - Mechanical Engineering Dept, University of Alberta（工程学院-机械工程系，阿尔伯塔大学）
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University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
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