I developed something similar for my PhD thesis, which works in a decentralized way using an ad-hoc network: http://thesis.marcoslot.net/merrion.htm http://thes

I developed something similar for my PhD thesis, which works in a decentralized way using an ad-hoc network: http://thesis.marcoslot.net/merrion.htm http://thesis.marcoslot.net/merrion.htm (press play, you can click cars and enable visualizations) The system analyzes the driving tracks in the map and finds potential conflict areas. Cars require permission from other cars they might conflict with to enter into a conflict area. To deal with communication failure, they also need confirmation that there are no other cars in the (relevant) area than the ones that responded, which they can determine by sharing and combining LIDAR data and correcting the observations for delays. When receiving a request from another car, a car can decide to respond with accept (if no conflict), reject (if the responder is competing for the same area and has a lower eta or identifier), or tentatively accept (if the responder already got confirmation), which builds a directed, acyclic graph determining the order in which cars can cross. Cars build a trajectory through all the upcoming conflict areas and can enter the next conflict area in the trajectory if all the cars that tentatively accepted have passed it in their trajectory. If this cannot be confirmed through messages from those cars, LIDAR can again be used to confirm that the preceding cars left the area (or did not enter before the deadline). It improves efficiency quite a lot, though it's slightly academic in the sense that it assumes there are only autonomous cars and no radio jammers or liars. Everything freezes if there is a human car, since safety cannot be confirmed, though this can be solved by combining it with (more probabilistic) recognition algorithms. The thesis mostly focuses on the distributed systems aspect and how to preserve safety in case of communication failure. There's some ongoing work on applying it to lane changing scenarios, where it can be effective even if only a relatively small percentage of cars support it.