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Fully-Automated Verification of Linear Systems Using Reachability Analysis with Support Functions
No full textData-Driven Vehicle Dynamics: Lever-Aging SINDy for Optimization-Based Vehicular Motion Planning
No full textMotion planning remains a crucial challenge for the widespread adoption of autonomous vehicles. This paper presents a novel approach that integrates an empirical plant model within an optimization-based motion planning architecture. The model prioritizes performance and efficiency while maintaining interpretability. We introduce a methodology that utilizes a data-driven approach to derive an interpretable description of the evolution of vehicle states over time using sparse regression. This method allows effective learning from limited datasets, eliminating the need for extensive and expensive data collection. Our approach addresses the trade-off between performance and accuracy, enabling adaptation to diverse driving scenarios. We affirm the efficacy of our methodology via an extensive analysis, evaluating the independent prediction performance across diverse metrics. Additionally, we examine the overall tracking performance when incorporated into an optimization-based framework. Finally, we present a comparative analysis and discuss the subsequent impact on overall motion planning and decision-making in relation to a state-of-the-art single-track model
Sustainability Assessment and Optimization in Construction Site: A Simulation-Based Approach
No full textThe construction industry is currently facing significant challenges. In order to address these challenges, the REMUS simulation model library for the construction industry is being developed. To this end, the physical modules are divided into stationary and mobile modules, as well as information objects. To create the simulation model and conduct the simulation experiment, a requirements cluster with the most important parameters of construction sites is created. The elements of sustainability—environmental, economy, and social aspects—are employed to assess the simulation results and to optimize the model. To this end, corresponding KPIs, methods, and procedures are delineated, which are documented during the various simulation experiments and evaluated subsequently. The equipment and environment exert an influence on the “economy”. This is reflected in the costs associated with the model components and their operation. The area of “environmental” is represented by the consumption of input materials. Alternative consumption and recovery concepts are implemented and compared here. The “social” aspect is represented by the human-machine collaboration. As part of the simulation experiments, the recorded variables are continuously adapted and refined. This process enables the simulation to improve the sustainability of the construction site environment