SMART VEHICLE
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 operate a vehicle, comprising:
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.
2 . The method of claim 1 , wherein the 3D model of each sensor's field of view is based on a pre-determined model of the given sensor's unobstructed field of view.
3 . The method of claim 1 , wherein the 3D model for each sensor's field of view is based on the given sensor's location and orientation relative to the vehicle.
4 . The method of claim 1 , wherein the weather information is received from a remote computer or from one of the plurality of sensors.
5 . The method of claim 1 , wherein: at least one model of the plurality of 3D models includes probability data indicating a probability of detecting an object at a given location of the at least one model, and this probability data is used when aggregating the plurality of 3D models to generate the comprehensive 3D model.
6 . The method of claim 1 , wherein: the detailed map information includes probability data indicating a probability of detecting an object at a given location of the map, and this probability data is used when combining the comprehensive 3D model with detailed map information.
7 . The method of claim 1 , wherein combining the comprehensive 3D model with detailed map information results in a model of the vehicle's environment annotated with information describing whether various portions of the environment are occupied, unoccupied, or unobserved.
8 . The method of claim 1 , wherein information from neighboring vehicles is communicated using a vehicle-to-vehicle transceiver.
9 . The method of claim 1 , comprising
detecting a vehicle tail-gating the first vehicle by detecting a distance or a time period separating the first and second vehicle; and
modifying a buffer zone separating the first and second vehicles to avoid tail-gating.
10 . The method of claim 1 , comprising detecting a new road from a positioning system, capturing street view images of the new road, and updating a digital map with the new road coordinates and street view images.
11 . A method for navigating a first vehicle, comprising:
generating a 3D model of a sensor's field of view;
detecting a vehicle tail-gating the first vehicle by detecting a distance or a time period separating the first and second vehicle;
modifying a buffer zone separating the first and second vehicles to avoid tail-gating.
12 . The method of claim 11 , wherein the 3D model of each sensor's field of view is based on a pre-determined model of the given sensor's unobstructed field of view.
13 . The method of claim 11 , wherein the 3D model for each sensor's field of view is based on the given sensor's location and orientation relative to the vehicle.
14 . The method of claim 11 , wherein the weather information is received from a remote computer or from one of the plurality of sensors.
15 . The method of claim 11 , wherein: at least one model of the plurality of 3D models includes probability data indicating a probability of detecting an object at a given location of the at least one model, and this probability data is used when aggregating the plurality of 3D models to generate the comprehensive 3D model.
16 . The method of claim 11 , wherein: the detailed map information includes probability data indicating a probability of detecting an object at a given location of the map, and this probability data is used when combining the comprehensive 3D model with detailed map information.
17 . The method of claim 11 , wherein combining the comprehensive 3D model with detailed map information results in a model of the vehicle's environment annotated with information describing whether various portions of the environment are occupied, unoccupied, or unobserved.
18 . The method of claim 11 , wherein information from neighboring vehicles is communicated using a vehicle-to-vehicle transceiver.
19 . The method of claim 11 , comprising:
receiving information from neighboring vehicles, traffic information, and weather information;
adjusting one or more characteristics of the plurality of 3D models based on the received traffic and weather information and tailgating information;
aggregating the plurality of 3D models to generate a comprehensive 3D model;
combining the comprehensive 3D model with detailed map information; and using the combined comprehensive 3D model with detailed map information to maneuver the vehicle.
20 . The method of claim 11 , comprising detecting a new road from a positioning system, capturing street view images of the new road, and updating a digital map with the new road coordinates and street view images.