Connected and Automated Vehicles (CAVs) rely on accurate position and vehicle kinematics information to perform various driving tasks, including vehicle following, lane change, and collision avoidance. However, there is a risk of malicious vehicles intentionally or unintentionally sending false sensor information to other vehicles, which can lead to traffic disruptions or even loss of human lives. To address this issue, the authors propose leveraging cloud-computing to enhance the resilience of CAVs against malicious vehicles. The idea is for each vehicle to share its local sensor information with other vehicles, creating redundancy in the cloud. This redundancy is then exploited through a sensor fusion algorithm implemented in the cloud. The proposed sensor fusion algorithm aims to provide a robust state estimation of all vehicles in the cloud, assuming that the number of malicious information is relatively small. By combining data from multiple sources and cross-referencing them, the algorithm can identify and isolate malicious vehicles. To demonstrate the effectiveness of their approach, numerical examples are provided which showcase how the proposed estimator successfully detects and isolates malicious vehicles while maintaining accurate state estimation for all other vehicles. Overall, this research presents a novel solution for enhancing the security and reliability of CAVs by leveraging cloud-computing and information redundancy. By effectively detecting and isolating malicious vehicles, this approach has significant potential in ensuring safer and more efficient autonomous driving systems.
Connected and Automated Vehicles (CAVs) are cars that can drive themselves and communicate with each other. They need accurate information about where they are and how they move to drive safely. Some cars can send fake information to cause problems or even harm people. The authors suggest using cloud-computing, which means storing and processing data on the internet, to make CAVs more secure. Cars can share their sensor information in the cloud to have backups in case something goes wrong. A special computer program in the cloud combines data from different sources to find any bad cars and keep the others safe. The research shows that this new method works well by giving examples of detecting bad cars while still knowing where all the other cars are."
Definitions
- Connected and Automated Vehicles (CAVs): Cars that can drive themselves and communicate with each other.
- Malicious vehicles: Cars that send false information to cause problems or harm people.
- Cloud-computing: Storing and processing data on the internet instead of a local computer.
- Sensor information: Data collected by sensors in the car, like cameras or radars, to understand what's happening around it.
- Redundancy: Having backups or extra copies of something for safety.
- Sensor fusion algorithm: A computer program that combines data from different sensors to get a better understanding of what's happening.
- State estimation: Knowing where something is and how it's moving based on available information.
Enhancing the Security and Reliability of Connected and Automated Vehicles with Cloud-Computing
Connected and automated vehicles (CAVs) are becoming increasingly popular as they offer a wide range of benefits, including improved safety, efficiency, and convenience. However, these vehicles rely on accurate position and vehicle kinematics information to perform various driving tasks such as vehicle following, lane change, and collision avoidance. Unfortunately, there is a risk of malicious vehicles intentionally or unintentionally sending false sensor information to other vehicles which can lead to traffic disruptions or even loss of human lives. To address this issue, researchers have proposed leveraging cloud-computing to enhance the resilience of CAVs against malicious vehicles.
The Proposed Solution
The idea behind the proposed solution is for each vehicle to share its local sensor information with other vehicles in the cloud creating redundancy in the system. This redundancy is then exploited through a sensor fusion algorithm implemented in the cloud that provides robust state estimation for all vehicles assuming that only a small number are malicious. The algorithm combines data from multiple sources while cross-referencing them so it can identify and isolate malicious vehicles while maintaining accurate state estimation for all other cars.
Demonstrating Effectiveness
To demonstrate how effective their approach is at detecting malicious behavior while providing accurate state estimation for all other cars, numerical examples were provided by researchers showing how their proposed estimator successfully detects and isolates malicious vehicles while maintaining accuracy for all others.
Conclusion
Overall this research presents a novel solution for enhancing the security and reliability of CAVs by leveraging cloud-computing and information redundancy. By effectively detecting and isolating malicious vehicles this approach has significant potential in ensuring safer autonomous driving systems as well as more efficient ones too.
Connected and Automated Vehicles (CAVs) rely on the correctness of position and other vehicle kinematics information to fulfill various driving tasks such as vehicle following, lane change, and collision avoidance. However, a malicious vehicle may send false sensor information to the other vehicles intentionally or unintentionally, which may cause traffic inconvenience or loss of human lives. Here, we take the advantage of cloud-computing and increase the resilience of CAVs to malicious vehicles by assuming each vehicle shares its local sensor information with other vehicles to create information redundancy on the cloud side. We exploit this redundancy and propose a sensor fusion algorithm for the cloud, capable of providing a robust state estimation of all vehicles in the cloud under the condition that the number of malicious information is sufficiently small. Using the proposed estimator, we provide an algorithm for isolating malicious vehicles. We use numerical examples to illustrate the effectiveness of our methods.
