V2X with 5G/6G Image Exchange and AI-Based Viewpoint Fusion
Autonomous vehicles are required to communicate with each other in 5G or 6G, to avoid hazards, mitigate collisions, and facilitate the flow of traffic. However, for cooperative action, each vehicle must determine the wireless address and position of other vehicles in proximity, so that they can communicate directly with each other. It is not sufficient to know the wireless address alone; the wireless address must be associated with an actual vehicle in view. Methods disclosed herein enable vehicles to simultaneously acquire 360-degree images of other vehicles in traffic, and transmit those images wirelessly along with their wireless addresses. The various images are then “fused” by identifying objects that are viewed from at least two directions, and calculating their positions by triangulation. The resulting traffic map, or a listing of the vehicle positions, is then broadcast along with the wireless addresses of the vehicles The vehicles can then determine which wireless address belongs to which of the vehicles in proximity, and can thereby cooperate with each other to avoid accidents and facilitate the flow of traffic.
1 . A method for a first vehicle to communicate with a second vehicle, the second vehicle proximate to a third vehicle, the method comprising:
a. broadcasting a planning message specifying a particular time;
b. at the particular time, acquiring a first image depicting the second vehicle and the third vehicle;
c. receiving, from the second vehicle, an imaging message comprising a second image, the second image acquired by the second vehicle at the particular time, the second image depicting the first vehicle and the third vehicle; and
d. determining, according to the first image and the second image, a coordinate listing comprising a position of the first vehicle, a position of the second vehicle, and a position of the third vehicle.
2 . The method of claim 1 , wherein the planning message and the imaging message are transmitted according to 5G or 6G technology.
3 . The method of claim 1 , wherein the second image further includes an indication of a direction of travel of the second vehicle.
4 . The method of claim 1 , further comprising:
a. determining, from the imaging message, a wireless address of the second vehicle; and
b. adding, to the coordinate listing, the wireless address of the second vehicle and a wireless address of the first vehicle.
5 . The method of claim 1 , further comprising:
a. measuring a distance from the first vehicle to either the second vehicle or the third vehicle; and
b. determining the coordinate listing according to the distance.
6 . The method of claim 1 , further comprising:
a. providing, according to the coordinate listing, a traffic map comprising a two-dimensional image indicating the position of the first vehicle, the position of the second vehicle, and the position of the third vehicle; and
b. indicating, on the traffic map, a wireless address of the first vehicle.
7 . The method of claim 1 , wherein the imaging message further indicates at least one of a vehicle type, a color, or a lane position of the second vehicle.
8 . The method of claim 1 , wherein the coordinate listing further indicates at least one of a vehicle type, a color, or a lane position of the first vehicle.
9 . The method of claim 1 , further comprising broadcasting the coordinate listing.
10 . The method of claim 1 , further comprising:
a. determining that a traffic collision with the second vehicle is imminent;
b. determining, according to the coordinate listing, which wireless address corresponds to the second vehicle; and
c. transmitting, to the second vehicle, an emergency message.
11 . The method of claim 1 , wherein the coordinate listing includes a fourth vehicle which is not depicted in the first image.
12 . The method of claim 1 , further comprising:
a. acquiring a plurality of images of vehicles in traffic;
b. providing the plurality of images to a computer containing an artificial intelligence model; and
c. determining, according to the artificial intelligence model, a predicted coordinate listing comprising predicted positions of the vehicles.
13 . The method of claim 12 , further comprising:
a. acquiring a further image of further vehicles in traffic;
b. receiving at least one message from at least one proximate vehicle, the at least one message comprising an additional image of the vehicles in traffic;
c. providing the further image and the additional image as input to an algorithm based at least in part on the artificial intelligence model; and
d. determining, as output from the algorithm, an updated coordinate listing comprising predicted positions of the further vehicles.
14 . Non-transitory computer-readable media in a second vehicle, the second vehicle in traffic, the traffic comprising a first vehicle and at least one other vehicle, the media containing instructions that when implemented by a computing environment cause a method to be performed, the method comprising:
a. receiving, from the first vehicle, a planning message specifying a time;
b. acquiring, at the specified time, an image comprising the first vehicle and the at least one other vehicle;
c. transmitting, to the first vehicle, an imaging message comprising the image; and
d. receiving, from the first vehicle, a coordinate listing or a traffic map comprising positions of the first vehicle, the second vehicle, and the at least one other vehicle.
15 . The media of claim 14 , the method further comprising:
a. determining, for each of the first, second, and third vehicles, a vehicle type or a vehicle color; and
b. transmitting, to the first vehicle, a message comprising the determined vehicle types or vehicle colors.
16 . The media of claim 14 , the method further comprising transmitting, to the first vehicle, a wireless address of the second vehicle.
17 . The media of claim 16 , wherein:
a. the coordinate listing or the traffic map further indicates, in association with the position of the second vehicle, the wireless address of the second vehicle; and
b. the coordinate listing or the traffic map further indicates, in association with the position of the first vehicle, a wireless address of the first vehicle.
18 . A computer containing an artificial intelligence structure comprising;
a. one or more inputs, each input comprising an image of traffic, the traffic comprising a plurality of vehicles;
b. one or more internal functions, each internal function operably linked to one or more of the inputs; and
c. an output operably linked to the one or more of the internal functions, the output comprising a prediction of a two-dimensional position of each vehicle of the plurality.
19 . The computer of claim 18 , the artificial intelligence structure further comprising one or more adjustable variables associated with the one or more internal functions, the one or more adjustable variables adjusted by supervised learning according to a plurality of individually recorded inputs.
20 . The computer of claim 18 , further comprising an algorithm, based at least in part on the artificial intelligence structure, the algorithm configured to take, as input, one or more images of further vehicles in traffic, and to provide, as output, a two-dimensional position of each of the further vehicles.