Collision detection system and method of operation

Abstract

A system for detecting driver vehicle travelling in an unsafe manner comprising a radar system configured to generate a sequence of frames of radar data. A target trajectory system configured to receive the sequence of frames of radar data and to generate target trajectory data for a vehicle. An alarm system configured to receive the target trajectory data and to generate an alarm as a function of the target trajectory data, a probability of collision, a degree of erratic driving or other suitable data.

1 - 22 . (canceled) 
     
     
         23 . A method comprising:
 generating a sequence of frames of sensor data using a sensor system;   receiving the sequence of frames of sensor data at a processor;   generating target data from the sequence of frames of sensor data using the processor;   generating target trajectory data from the target data using the processor and a probability density function;   generating a signal as a function of the target trajectory data.   
     
     
         24 . The method of  claim 23  further comprising:
 generating location data for a vehicle using the sensor data; and 
 modifying the target trajectory data as a function of the location data. 
 
     
     
         25 . The method of  claim 23  further comprising:
 generating road map data; and 
 modifying the target trajectory data as a function of the road map data. 
 
     
     
         26 . The method of  claim 23  further comprising:
 generating a first audio indicator for a vehicle approaching from a front of a patrol vehicle; and 
 generating a second audio indicator for a vehicle approaching from a rear of the patrol vehicle. 
 
     
     
         27 . The method of  claim 23  further comprising increasing or decreasing a tone frequency of an audio indicator as a function of a location a vehicle. 
     
     
         28 . The method of  claim 23  further comprising increasing or decreasing a volume of an audio indicator as a function of a location of a vehicle. 
     
     
         29 . The method of  claim 23  further comprising changing a cadence of an audio indicator as a function of a location of a vehicle. 
     
     
         30 . The method of  claim 23  wherein generating the target trajectory data comprises applying a fuzziness factor to the target trajectory data as a function of vehicle weaving data. 
     
     
         31 . The method of  claim 23  wherein generating the target trajectory data comprises extrapolating a vehicle position for a vehicle as a function of historical location data for the vehicle. 
     
     
         32 . The method of  claim 23  wherein generating the target trajectory data comprises extrapolating a vehicle elevation for a vehicle as a function of historical elevation data for the vehicle. 
     
     
         33 . The method of  claim 23  wherein generating the target trajectory data comprises:
 generating a first probability density function for a vehicle at a first time; 
 generating a second probability density function for the vehicle at a second time; and 
 determining whether the first probability density function is different from the second probability density function. 
 
     
     
         34 . The method of  claim 23  further comprising determining a type of impairment as a function of target trajectory data of a vehicle. 
     
     
         35 . The method of  claim 23  wherein generating the target trajectory data comprises generating a probability density function corresponding to a probability of points of impact at an intersection of predicted target trajectory data with a vehicle outline. 
     
     
         36 . The method of  claim 23  wherein generating the target trajectory data comprises applying a fuzziness factor that increases a standard deviation of a probability density function as a function of vehicle weaving and erratic target trajectory data. 
     
     
         37 . The method of  claim 23  wherein generating the target trajectory data for the vehicle using the processor comprises applying a fuzziness factor that increases a standard deviation of a probability density function as a function of a target trajectory deviating from a normal path as indicated from road map information. 
     
     
         38 . A system comprising:
 a sensor system configured to generate a sequence of frames of sensor data;   a processor configured to receive the sequence of frames of sensor data and to generate target data from the sequence of frames of sensor data; and   the processor further configured to generate target trajectory data from the target data using a probability density function and a signal as a function of the target trajectory data.   
     
     
         39 . The system of  claim 38  wherein the processor is further configured to generate location data for a vehicle using the sensor data and to modify the target trajectory data as a function of the location data. 
     
     
         40 . The system of  claim 38  wherein the processor is further configured to generate road map data and to modify the target trajectory data as a function of the road map data. 
     
     
         41 . The system of  claim 38  wherein the processor is further configured to generate a first audio indicator for a vehicle approaching from a front of a patrol vehicle and to generate a second audio indicator for a vehicle approaching from a rear of the patrol vehicle. 
     
     
         42 . The system of  claim 38  wherein the processor is further configured to increase or decrease a tone frequency of an audio indicator as a function of a location a vehicle. 
     
     
         43 . In a system comprising a sensor system configured to generate a sequence of frames of sensor data, a processor configured to receive the sequence of frames of sensor data and to generate target data from the sequence of frames of sensor data, the processor further configured to generate target trajectory data from the target data using a probability density function and a signal as a function of the target trajectory data, the processor is further configured to generate location data for a vehicle using the sensor data and to modify the target trajectory data as a function of the location data, the processor is further configured to generate road map data and to modify the target trajectory data as a function of the road map data, the processor is further configured to generate a first audio indicator for a vehicle approaching from a front of a patrol vehicle and to generate a second audio indicator for a vehicle approaching from a rear of the patrol vehicle, and the processor is further configured to increase or decrease a tone frequency of an audio indicator as a function of a location a vehicle, a method comprising:
 generating the sequence of frames of sensor data using the sensor system; 
 receiving the sequence of frames of sensor data at the processor; 
 generating the target data from the sequence of frames of sensor data using the processor; 
 generating the target trajectory data from the target data using the processor and the probability density function; 
 generating the signal as the function of the target trajectory data; 
 generating the location data for the vehicle using the sensor data; 
 modifying the target trajectory data as the function of the location data; 
 generating the road map data; 
 modifying the target trajectory data as the function of the road map data; 
 generating the first audio indicator for the vehicle approaching from a front of a patrol vehicle; 
 generating a second audio indicator for a vehicle approaching from the rear of the patrol vehicle; 
 increasing or decreasing a tone frequency of an audio indicator as the function of the location the vehicle; 
 increasing or decreasing a volume of an audio indicator as the function of the location of the vehicle; 
 changing a cadence of an audio indicator as the function of the location of the vehicle; 
 wherein generating the target trajectory data comprises applying a fuzziness factor to the target trajectory data as a function of vehicle weaving data; 
 wherein generating the target trajectory data comprises extrapolating a vehicle position for the vehicle as a function of historical location data for the vehicle; 
 generating the target trajectory data comprises extrapolating a vehicle elevation for the vehicle as a function of historical elevation data for the vehicle; 
 wherein generating the target trajectory data comprises: 
 generating a first probability density function for the vehicle at a first time; 
 generating a second probability density function for the vehicle at a second time; and 
 determining whether the first probability density function is different from the second probability density function; 
 determining a type of impairment as a function of target trajectory data of a vehicle; 
 wherein generating the target trajectory data comprises generating a probability density function corresponding to a probability of points of impact at an intersection of predicted target trajectory data with a vehicle outline; 
 wherein generating the target trajectory data comprises applying a fuzziness factor that increases a standard deviation of a probability density function as a function of vehicle weaving and erratic target trajectory data; and 
 wherein generating the target trajectory data for the vehicle using the processor comprises applying a fuzziness factor that increases a standard deviation of a probability density function as a function of a target trajectory deviating from a normal path as indicated from road map information.