ACM Transactions on Cyber-Physical Systems

Electric Vehicles (EVs) represent a green alternative to traditional fuel-powered vehicles. To enforce their widespread use, both the technical development and the security of users shall be guaranteed. Users’ privacy represents a possible threat that ...

Automotive cyber-physical systems consist of multiple control subsystems working under resource limitations, and the trend is to run the corresponding control tasks on a shared platform. The resource requirements of the tasks are usually variable at ...

The lack of any sender authentication mechanism in place makes Controller Area Network (CAN) vulnerable to security threats. For instance, an attacker can impersonate an Electronic Control Unit (ECU) on the bus and send spoofed messages unobtrusively with ...

In vision-based object recognition systems, imaging sensors perceive the environment and then objects are detected and classified for decision-making purposes, e.g., to maneuver an automated vehicle around an obstacle or to raise alarms for intruders in ...

This work presents an experimental evaluation of the detection performance of eight different algorithms for anomaly detection on the Controller Area Network (CAN) bus of modern vehicles based on the analysis of the timing or frequency of CAN messages. ...

Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles’ practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as ...

Cooperative adaptive cruise control (CACC) is a smart transportation solution to alleviate traffic congestion and enhance road safety. The performance of CACC systems can be remarkably affected by communication time delays, and traditional control methods ...

Deep reinforcement learning (DRL) has shown good performance in tackling Markov decision process (MDP) problems. As DRL optimizes a long-term reward, it is a promising approach to improving the energy efficiency of data-center cooling. However, ...

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being ...

Incorporating learning based components in the current state-of-the-art cyber-physical systems (CPS) has been a challenge due to the brittleness of the underlying deep neural networks. On the bright side, if executed correctly with safety guarantees, this ...

Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or parameterized input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of ...

Autonomous vehicles are cyber-physical systems that combine embedded computing and deep learning with physical systems to perceive the world, predict future states, and safely control the vehicle through changing environments. The ability of an autonomous ...

Deep neural networks (DNNs) are effective tools for learning-enabled cyber-physical systems (CPSs) that handle high-dimensional image data. However, DNNs may make incorrect decisions when presented with inputs outside the distribution of their training ...