Differential analysis of navigation satellite signals by 5G/6G messaging
In traffic, vehicle safety generally depends on the relative separation between two vehicles, and not at all on their absolute geographical positions. Therefore, methods are disclosed for determining the relative separation differentially. Two vehicles simultaneously acquire the same satellite signals, reduce the signals to data, and then transmit the data, preferably with fast 5G or 6G, to one of the vehicles or to a roadside access point. A computer then analyzes the data based on differences between the two sets of signals, thereby determining the separation distance and angle between the two vehicles. Other vehicles within range can do the same. The relative locations of all the participating vehicles can then be broadcast, in the form of a table or a map, to all the vehicles in proximity. Improved position measurements can enable collision avoidance and harm minimization in traffic.
1 . A method for a processor comprising an AI (artificial intelligence) model to determine a distance between vehicles, the method comprising:
a) selecting, by a first vehicle, a plurality of times and frequencies according to a planned transmission schedule of a global navigation satellite system;
b) transmitting, by the first vehicle, to a second vehicle, a planning message specifying the plurality of times and frequencies;
c) acquiring, by the first and second vehicles, simultaneously, at times and frequencies according to the plurality of times and frequencies, navigation satellite signals;
d) transmitting, by the second vehicle, measurement data derived from the navigation satellite signals;
e) providing, as input to the AI model, the navigation satellite signals acquired by the first vehicle and the measurement data transmitted by the second vehicle; and
f) determining, as output from the AI model, a two-dimensional position of the second vehicle relative to the first vehicle.
2 . The method of claim 1 , wherein the AI model is a neural net.
3 . The method of claim 1 , wherein the AI model is a hidden Markov model.
4 . The method of claim 1 , wherein the processor comprising the AI model is in the first vehicle.
5 . The method of claim 1 , wherein the processor comprising the AI model is in a fixed location and is in wireless communication with the first vehicle.