Cyber-physical systems (CPS) research continues to advance the integration of computation, communication, and control into safety-critical and large-scale infrastructures. The ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), held as part of CPS-IoT Week, has long been a premier venue for disseminating these advances and fostering interdisciplinary dialogue. This special issue contains a selection of original papers, which extend earlier results presented at the 15th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) that took place in Hong Kong from May 13–16, 2024, as part of CPS-IoT Week 2024. They cover different aspects of CPS research, reflecting both methodological rigor and practical impact.

Scenario Description Languages (SDLs) serve as high-level encodings, offering an interpretable representation of traffic situations encountered by autonomous vehicles (AVs). Their utility extends to critical safety analyses, such as identifying analogous traffic scenarios within vast AV datasets, and aiding in real-to-simulation transfers. This paper addresses the challenging task of autonomously deriving SDL embeddings from AV data. We introduce the Scenario2Vector method, leveraging video transformers to automatically detect spatio-temporal actions of the ego AV through front-camera video footage. Our methodology draws upon the Berkeley Deep Drive - eXplanations (BDD-X) dataset. To determine ground truth actions of the ego AV, we employ BERT combined and dependency grammar-based trees, utilizing the resulting labels for Scenario2.Vector training. Our approach is benchmarked against a 3D convolution (C3D)-based method and a transfer-learned video transformer (ViViT) model, evaluating both extraction accuracy and scenario retrieval capabilities. The results reveal that Scenarioz'Vector is highly effective in detecting ego vehicle actions from video input, adeptly handling traffic scenarios with multiple ego vehicle maneuvers.

This paper describes RACECAR, the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data was collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge (2021–2022) have contributed to this dataset. The dataset spans 11 racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The dataset contains 27 racing sessions across 11 scenarios with over 6.5 hours of autonomous racing. The data has been released in both ROS 2 and nuScenes format. We have also developed the ROSbag2nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing. We present several benchmark problems on localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping to explore issues that arise at the limits of operation of the vehicle. RACECAR data can be accessed at https://github.com/linklab-uva/RACECAR_DATA.

We consider the challenging problem of high speed autonomous racing in realistic dynamic environments. DeepRacing is a novel end-to-end framework, and a virtual testbed for training and evaluating algorithms for autonomous racing. The virtual testbed is implemented using the realistic Formula One (F1) Codemasters game, which is used by many F1 drivers for training. We present AdmiralNet - a Convolution Neural Network (CNN) integrated with Long Short-Term Memory (LSTM) cells that can be tuned for the autonomous racing task in the highly realistic F1 game. We evaluate AdmiralNet’s performance on unseen race tracks, and also evaluate the degree of transference between the simulation and the real world by implementing end-to-end racing on a physical 1/10 scale autonomous racecar.

Model Predictive Control (MPC) plays an important role in optimizing operations of complex cyber-physical systems because of its ability to forecast system’s behavior and act under system level constraints. However, MPC requires reasonably accurate underlying models of the system. In many applications, such as building control for energy management, Demand Response, or peak power reduction, obtaining a high-fidelity physics-based model is cost and time prohibitive, thus limiting the widespread adoption of MPC. To this end, we propose a data-driven control algorithm for MPC that relies only on the historical data. We use multi-output regression trees to represent the system’s dynamics over multiple future time steps and formulate a finite receding horizon control problem that can be solved in real-time in closed-loop with the physical plant. We apply this algorithm to peak power reduction in buildings to optimally trade-off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics.

A building testbed for design and evaluation of energy efficient control and demand response strategies for real buildings is presented. The testbed is a scaled down model of a centralized Heating, Ventilation and Air Conditioning (HVAC) and lighting system. Sensing and control in the tesbed is achieved using the standard Building Automation and Control Network protocol. A MATLAB based front-end can be used to run and observe experiments.

In large building systems, such as a university campus, the air-conditioning systems are commonly served by chiller plants, which contribute a large fraction of the total electricity consumption of the campuses. The power consumption of a chiller is highly affected by its Coefficient of Performance (COP), which is optimal when the chiller is operated at or near full load. For a chiller plant, its overall COP can be optimized by utilizing a Thermal Energy Storage (TES) and switching its operation between COP-optimal charging and discharging modes. However, uncoordinated mode switchings of chiller plants may cause temporally-correlated high electricity demand when multiple plants are charging their TES concurrently. In this paper, a GS approach, proposed in our previous work, is used to schedule the chiller plants to reduce their peak aggregate power demand while ensuring safe operation of the TES. We present a scheduling algorithm based on backward reach set computation of the TES dynamics. The proposed algorithm is demonstrated in a numerical simulation in Mat lab to be effective for reducing the peak power demand and the overall electricity cost.

Peak power consumption of buildings in large facilities like hospitals and universities becomes a big issue because peak prices are much higher than normal rates. During a power demand surge an automated power controller of a building may need to schedule ON and OFF different environment actuators such as heaters and air quality control while maintaining the state variables such as temperature or air quality of any room within comfortable ranges. The green scheduling problem asks whether a scheduling policy is possible for a system and what is the necessary and sufficient condition for systems to be feasible. In this paper we study the feasibility of the green scheduling problem for HVAC(Heating, Ventilating, and Air Conditioning) systems which are approximated by a discrete-time model with constant increasing and decreasing rates of the state variables. We first investigate the systems consisting of two tasks and find the analytical form of the necessary and sufficient conditions for such systems to be feasible under certain assumptions. Then we present our algorithmic solution for general systems of more than 2 tasks. Given the increasing and decreasing rates of the tasks, our algorithm returns a subset of the state space such that the system is feasible if and only if the initial state is in this subset. With the knowledge of that subset, a scheduling policy can be computed on the fly as the system runs, with the flexibility to add power-saving, priority-based or fair sub-policies.