Smart vehicle

Abstract

A method for controlling a vehicle includes generating a three dimensional (3D) model of the vehicle and neighboring objects and deriving an environment for the vehicle; predicting a driver behavior based on at least the 3D model, a current state of the vehicle and the environment of the vehicle; and returning vehicle control from a computer control to a driver control based on the predicted driver behavior. The system provides a seamless way to transfer control from human to computer and vice versa.

What is claimed is: 
     
         1 . A method for controlling a vehicle, the method comprising:
 generating a three dimensional (3D) model of the vehicle and neighboring objects and deriving an environment for the vehicle;   predicting a driver behavior based on at least the 3D model, a current state of the vehicle and the environment of the vehicle; and   returning vehicle control from a computer control to a driver control based on the predicted driver behavior.   
     
     
         2 . The method of  claim 1 , wherein the object is a neighboring vehicle, comprising
 determining if the neighboring vehicle is performing the predicted behavior based on the 3D model and a current state of the vehicle and the environment of the vehicle; and   preparing the vehicle to respond based at least in part on the predicted driver behavior of the neighboring vehicle and the driver and controlling the vehicle in response.   
     
     
         3 . The method of  claim 1 , wherein the detected object includes one of an automobile, a pedestrian, structure, or a bicycle. 
     
     
         4 . The method of  claim 1 , wherein the object comprises at least one of: location, traffic lane in which the detected object is traveling, speed, acceleration, entry onto a road, exit off of a road, activation of headlights, activation of taillights, or activation of blinkers. 
     
     
         5 . The method of  claim 1 , comprising providing a command to orient the vehicle includes positioning the vehicle at a predetermined distance from the detected object, the predetermined distance being based, at least in part, on the classification of the neighboring vehicle. 
     
     
         6 . The method of  claim 1 , comprising sending the likely behavior of the neighboring vehicle to nearby vehicles using vehicle-to-vehicle communication and wherein a plurality of vehicles form a flock. 
     
     
         7 . The method of  claim 1 , comprising predicting driving behavior of the neighboring vehicle with a statistical method using a hidden Markov model. 
     
     
         8 . The method of  claim 1 , comprising generating models based on driving data collected from a population of drivers and captured from a crowd (crowd-sourced). 
     
     
         9 . The method of  claim 1 , comprising predicting a driver behavior in response to a behavior of the neighboring object based on prior behavior data and destination. 
     
     
         10 . The method of  claim 1 , comprising releasing computer control of one or more of: a steering wheel, a turning signal, an accelerator pedal and a brake pedal. 
     
     
         11 . The method of  claim 1 , comprising receiving data from sensors in the vehicle, global positioning data and vehicle-to-vehicle data. 
     
     
         12 . The method of  claim 1 , comprising acquiring a current position of the vehicle; acquiring a driving route to be followed by the vehicle; acquiring driving data including information on a speed of the vehicle and information on each vehicle operation performed by a driver; predicting the driving behavior of the driver at the object point based on driving history. 
     
     
         13 . The method of  claim 1 , wherein the vehicle is in a first lane, the neighboring vehicle is in a second lane, and the at least one predicted behavior comprises the neighboring vehicle changing lanes from the second lane to the first lane in front of the target vehicle. 
     
     
         14 . The method of  claim 1 , wherein the current state of the vehicle comprises the vehicle being in the first lane. 
     
     
         15 . The method of  claim 14 , wherein the current state of the environment of the vehicle comprises the neighboring vehicle being in the second lane and the neighboring vehicle overtaking the vehicle. 
     
     
         16 . The method of  claim 14 , wherein the current state of the environment of the vehicle further comprises another vehicle being in the second lane ahead of the neighboring vehicle. 
     
     
         17 . The method of  claim 14 , comprising controlling the vehicle to slow down. 
     
     
         18 . The method of  claim 1 , wherein the at least one predicted behavior further comprises the neighboring vehicle changing speed. 
     
     
         19 . The method of  claim 1 , comprising determining a confidence level for the predicted behavior of a neighboring vehicle and a confidence level for the predicted driver behavior. 
     
     
         20 . The method of  claim 1 , comprising computer-controlling the vehicle by causing the vehicle to perform an action comprising at least one of accelerating, decelerating, changing heading, changing lanes, shifting within a current lane, or providing a warning notification.