A smart vehicle can be operated by generating a 3D model of a sensor's field of view; receiving information from neighboring vehicles to compensate for blindspots in the sensor's field of view and in a driver's field of view; receiving traffic information, weather information; adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and blindspot information; aggregating the plurality of 3D models to generate a comprehensive 3D model; and combining the comprehensive 3D model with detailed map information; and using the combined comprehensive 3D model with detailed map information to maneuver the vehicle.
1. A method to follow a lane of a road with a smart vehicle, comprising:
creating with a processor a 3D model having x, y, and z coordinates of the road comprising the lane, a nearby vehicle, and the smart vehicle;
obtaining lane information that provides an estimated location of the lane; controlling the smart vehicle based on the obtained lane information
estimating a path of each neighboring vehicle and updating the 3D model of the road, the smart vehicle and the nearby vehicle; and
when the lane information becomes unavailable or unreliable, controlling the smart vehicle to maintain a relative position of the smart vehicle using the 3D model of the road and the nearby vehicle based on one or more estimated paths until the lane information becomes available or reliable again.
2. The method of claim 1 , wherein the lane information is based on at least one of a lane marking on the road, a geographic location of the vehicle, and a predetermined map of the road.
3. The method of claim 1 , wherein determining that the lane information has become unavailable or unreliable comprises detecting that a confidence of the lane information is less than a predetermined threshold.
4. The method of claim 1 , wherein determining that the lane information has become unavailable or unreliable comprises detecting that a confidence of the lane information is less than a predetermined threshold for at least a predetermined amount of time.
5. The method of claim 1 , further comprising: periodically obtaining updated lane information; determining that the updated lane information has become reliable; and in response to determining that the updated lane information has become reliable, ceasing to control the vehicle based on the estimated paths of the 3D model and using reliable lane information instead.
6. The method of claim 5 , wherein determining that the updated lane information has become reliable comprises detecting that a confidence of the updated lane information is greater than a predetermined threshold.
7. The method of claim 1 , further comprising using radio triangulation, laser ranging or camera estimation to monitor a speed of the neighboring vehicle; and modifying a speed of the vehicle to be less than the speed of the neighboring vehicle.
8. The method of claim 1 , wherein extrapolating the trajectory comprises extrapolating the path of each neighboring vehicle with a constant curvature.
9. The method of claim 1 , further comprising:
identifying vehicles traveling to a common destination for a predetermined period;
organizing the vehicles into a flock; and
forming a flock with neighboring vehicles by applying a flocking behavior for each vehicle using a separation rule to avoid crowding vehicular neighbors, an alignment rule to steer towards average heading of vehicular neighbors, and a cohesion rule to maintain proximity to an average position of vehicular neighbors.
10. The method of claim 1 , comprising communicating with neighboring vehicles to determine a relative position of the vehicle to neighboring vehicles.
11. The method of claim 10 , comprising performing lane identification using flock information and maintaining a predetermined spacing between vehicles based on a plurality of predetermined flock behavior rules.
12. The method of claim 10 , comprising flocking the vehicles over a plurality of lanes.
13. The method of claim 10 , comprising maintaining two separate flocks of vehicles in opposite directions.
14. The method of claim 10 , wherein the common destination comprises a segment of a vehicle's final destination.
15. The method of claim 1 , where vehicles traveling a same route is determined using a vehicle to vehicle communication protocol for identifying peers based upon encoded signals during peer discovery in a peer to peer network.
16. The method of claim 1 , wherein the protocol is compatible with WiFi, cellular, or Proximity Services via LTE Device Broadcast.
17. The method of claim 1 , comprising direct signaling that partitions a time-frequency resource into a number of segments can be utilized to communicate an identifier within a peer discovery interval.
18. The method of claim 1 , wherein vehicles that share same driving segments broadcast expressions indicating their path(s), comprising detecting vehicles in the same segment as part of the proximity services for capturing and sharing crowd-sourced navigation data.
19. The method of claim 1 , comprising capturing crowd-sourced information relating to: closing of a lane; predicting an avoidance maneuver; predicting a congestion with respect to a segment of the route of the at least one vehicle; and predicting traffic light patterns.
20. The method of claim 1 , comprising determining an obstacle in the lane and changing the vehicle's path to avoid the obstacle, wherein the obstacle comprises rocks, a lane closure, an inoperative vehicle, or a vehicle in an accident.