Vehicle Platooning and Cooperative Adaptive Cruise Control Lecture

This guest lecture was part of the course "Security and Privacy in Control" from the faculty of Mechanical Engineering at Technical University Delft.
We discuss the topic of Vehicle platooning and Cooperative Adaptive Cruise Control.
The lecture starts from the basics of Adaptive Cruise Control (ACC) and then moves on to show the advantages of adding communication (Cooperative ACC). We then show the potential risks of a cyber-attack on the communication channel and discuss ways to mitigate the effects.
This lecture was held by Lyons Lorenzo, part of the Reliable Robot Control Lab, lead by Laura Ferranti.

[@michaelc2281]

Hi there, thanks for putting the lecture online, I was wondering about string stability of platonning. When considering this you always have a time-delay in the information shared. With this delay can be implemented in the transfer function:
T = 1/H*(D + G*K)/(1+G*K) (String Stability Complementary Sensitivity)
But how does this transfer function change when you have a platoon with differenc vehicles? Or doesn't it?

[@LorenzoLyons-pe1wz]

Hello, yes indeed delays are always an issue with any real robotics system. One Idea (more popular with a control community approach) would be to include the delay as you have mentioned in the transfer function x_(i+1)(s)/x_i(s) and ensure that string stability holds . The entity of this delay would need to be measured in the field. In practice delays are never constant and usually not even gaussianly distributed since they may build up when the communication network is more heavily used or some other event-based circumstance. So the idea is to ensure string stability assuming for a "worst case" fixed delay.
Another strategy (coming from a robotics perspective) is to include some delay compensation feature, such as reconstructing the actual vehicle state, like the position, based on forward propagating the received state accounting for the communication delay. Something like real_position_now = position_communicated + Velocity*time_delay. Here the time delay could be actually measured in real time by comparing the time stamps of the various messages.