초록 ▼

A vehicle awareness system for monitoring remote vehicles relative to a host vehicle. The vehicle awareness system includes at least one object sensing device and a vehicle-to-vehicle communication device. A data collection module is provided for obtaining a sensor object data map and vehicle-to-veh

A vehicle awareness system for monitoring remote vehicles relative to a host vehicle. The vehicle awareness system includes at least one object sensing device and a vehicle-to-vehicle communication device. A data collection module is provided for obtaining a sensor object data map and vehicle-to-vehicle object data map. A fusion module merges the sensor object data map and vehicle-to-vehicle object data map for generating a cumulative object data map. A tracking module estimates the relative position of the remote vehicles to the host vehicle.

대표청구항 ▼

1. A method of enhancing a host vehicle awareness system relative to remote vehicles, the host vehicle including at least one object sensing device for sensing objects remote from the host vehicle, the host vehicle further including a vehicle-to-vehicle communication system for exchanging vehicle da

1. A method of enhancing a host vehicle awareness system relative to remote vehicles, the host vehicle including at least one object sensing device for sensing objects remote from the host vehicle, the host vehicle further including a vehicle-to-vehicle communication system for exchanging vehicle data in vehicle-to-vehicle messages between remote vehicles and the host vehicle, the method comprising the steps of: generating a sensor object data map in response to the sensed objects;generating a vehicle-to-vehicle object data map in response to a vehicle-to-vehicle message;merging data from the vehicle-to-vehicle object data map and data from the sensor object data map in a data association and merging module to collectively determine relative positions of remote vehicles to the host vehicle, wherein merging data from the vehicle-to-vehicle object data map and data from the sensor object data map includes integrally fusing the respective data into a merged object observation map, wherein integrally fusing the respective data includes detecting a mismatch in the determined relative position between the data from the vehicle-to-vehicle object data map and the sensor object data map for a respective remote vehicle, the mismatch being identified by an associated sensor error in a covariance noise matrix, and wherein sensor error and sensor bias as identified by the covariance noise matrix is corrected in at least one of the respective data maps for cooperatively merging data in the respective object data maps;estimating the relative position of the remote vehicles to the host vehicle utilizing the merged data maps; andoutputting tracking data to safety related applications for identifying threat assessments to the host vehicle, the safety related applications actuating a safety countermeasure in the host vehicle based on the threat assessment determined by the safety related applications. 2. The method of claim 1 wherein estimating the relative position of remote vehicles includes generating feedback data from a filter tracker that is provided to a sensor and wireless module for improving an accuracy of the at least one object sensing device, the filter tracker generating and updating a tracking list of sensed objects relative to the position, speed, and orientation of the host vehicle, the sensor and wireless module constructs an observed situational map in response to the data generated from the sensor object data map and the vehicle-to-vehicle object data map. 3. The method of claim 1 further comprising the step of generating a tracking list of the surrounding vehicles in response to estimating the relative position of remote vehicles, the tracking list includes a vehicle position, speed, and yaw rate of the remote vehicles. 4. The method of claim 3 wherein the tracking list is provided as feedback for continuously determining the relative position of remote vehicles to the host vehicle. 5. The method of claim 3 wherein the tracking list is output as part of the tracking data output to the safety related applications. 6. The method of claim 1 further comprising the step of collecting vehicle dynamic information of the host vehicle for estimating the relative positioning of the host vehicle to the remote vehicles. 7. The method of claim 1 wherein the vehicle-to-vehicle object data map is obtained from GPS and wireless communication devices. 8. The method of claim 1 wherein errors are determined between the vehicle-to-vehicle object data map and the sensor object data map, wherein prioritization is given to the vehicle-to-vehicle object data map or the sensor object data map based on a current location of the vehicle. 9. The method of claim 1 wherein prioritization is given to the sensor object data map if the host vehicle is located in an urban-like location having GPS obstructions. 10. The method of claim 1 wherein the safety related applications use positioning of the remote vehicles and remote vehicle orientation to actuate driver awareness notifications. 11. The method of claim 1 wherein the step of merging the vehicle-to-vehicle object data map and the sensor object data map includes joint localization and tracking of the remote vehicles and host vehicle. 12. The method of claim 11 wherein the localization of the host vehicle is characterized by the fusion of data from a global positioning system and data from in-vehicle object detection sensors, and is represented by: [gHmH]=[CHgCHm]⁢XH+[vHgvHm] where gH, is the GPS measurements of the host vehicle,[mh]mH is a vector of the host vehicle wheel speed and yaw rate measurements, XH is a state of a host vehicle, CHg is the GPS measurement matrix, CHm is the in-vehicle sensor measurement matrix, and vHg and vHm are noise factors. 13. The method of claim 12 wherein the tracking of the remote vehicle is characterized by a fusion of data from a vehicle-to-vehicle sensor and data from in-vehicle object detection sensors, and is represented by: [xioHκ1⋮oHκk]=[I5CHκ1⋮CHκk]⁢Xi+[0u1⁢κ1+CHκ1⁢XH⋮u1⁢κk+CHκk⁢XH]+[vivHκ1⋮vHκk] where Xi is the state of a i-th remote vehicle, OHκk is the range, range rate, and azimuth angle measurements of the κk-th vehicle as measured by sensing device on the host vehicle, I5 is an identity matrix, u1κ1 is a vector defined in (8), vi is a noise factor of the host vehicle, vHκk is a noise factor of the host vehicle. 14. The method of claim 13 further comprising the step of generating a sparse matrix, the sparse matrix being represented by: [x1⋮xKoHκ1⋮oHκLHgHmH]=[I5…00⋮⋱⋮⋮0…I50CHκ1…0Cκ1H⋮⋱⋮⋮0…CHκLHCκLHH000CHg000CHm]⁡[X1⋮XKXH]+[0⋮0u1⁢κ1⋮u1⁢κLH00]+[v1⋮vKvHκ1⋮vHκLHvHgvHm]. 15. The method of claim 13 further comprising the step of determining a system dynamic equation, the system dynamic equation being represented by X(t+1)=ƒ(X, w) where X(t+1) is a prediction of the joint state; and w is a random variable representing un-modeled process noise. 16. The method of claim 13 wherein the linearization of the system dynamic equation is represented by: X(t+1)=ΦX+Gw+u2 where Φ is the Jacobian matrix of function ƒ with respect to X, G is the Jacobian matrix of function ƒ with respect to w, and the nonlinear term u2 is represented by the formula: u2=ƒ(X*,w*)−ΦX*−Gw*. 17. A vehicle awareness system for monitoring remote vehicles relative to a host vehicle, the vehicle awareness system comprising: at least one object sensing device;a vehicle-to-vehicle communication device;a data collection module for obtaining a sensor object data map and vehicle-to-vehicle object data map;a fusion module for fusing data from the sensor object data map and data from the vehicle-to-vehicle object data map for generating a merged situational object data map, wherein fusing the respective data includes detecting a mismatch in a determined relative position between the data from the vehicle-to-vehicle object data map and the data from the sensor object data map for a respective remote vehicle, the mismatch being identified by an associated sensor error in a covariance noise matrix, and wherein sensor error and bias as identified by the covariance noise matrix is corrected in at least one of the respective data maps for cooperatively merging data in the respective object data maps; anda tracking module for estimating the relative position of the remote vehicles to the host vehicle. 18. The system of claim 17 wherein the at least one object sensing device includes a radar-based sensing device. 19. The system of claim 17 wherein the at least one object sensing device includes a vision-based sensing device. 20. The system of claim 17 wherein the at least one object sensing device includes a light-based sensing device. 21. The system of claim 17 wherein the vehicle-to-vehicle communication device includes at least a GPS device and wireless communication module for communicating vehicle information between vehicles. 22. The system of claim 17 wherein the tracking device module includes a Kalman filter. 23. The system of claim 17 wherein the tracking device module includes a square root information filter. 24. The system of claim 17 wherein the tracking device module generates a tracking list that includes a vehicle position, speed, and yaw rate of the remote vehicle. 25. The system of claim 17 further comprising a feedback circuit coupled between the tracking device module and the data collection module, wherein the tracking device module generates feedback data to the data collection module via the feedback circuit for improving an accuracy of the at least one object sensing device. 26. The system of claim 17 further comprising at least one vehicle dynamic sensing device that provides vehicle dynamic data of the host vehicle for estimating the relative positioning of the host vehicle to the remote vehicles. 27. The system of claim 17 further comprising at least one safety related application for assessing a threat of remote vehicles to the host vehicle and for actuating a safety response.