초록 ▼

A computing architecture for a motorized land-based vehicle is disclosed. The computing architecture includes a data network comprised of a plurality of interconnected processors, a first group of sensors responsive to environmental conditions around the vehicle, a second group of sensors responsive

A computing architecture for a motorized land-based vehicle is disclosed. The computing architecture includes a data network comprised of a plurality of interconnected processors, a first group of sensors responsive to environmental conditions around the vehicle, a second group of sensors responsive to the vehicle's hardware systems, and a map database containing data that represent geographic features in the geographic area around the vehicle. A vehicle-environment modeling program, executed on the data network, uses the outputs from the first and second groups of sensors and the map database to provide and continuously update a data model that represents the vehicle and the environmental around the vehicle, including geographic features, conditions, structures, objects and obstacles around the vehicle. Vehicle operations programming applications, executed on the data network, use the data model to determine desired vehicle operation in the context of the vehicle's environment. A driver interface receives the vehicle driver's input. Vehicle control programming, executed on the data network, receives outputs from the vehicle operations programming applications and the driver interface, determines a resolved operation for the vehicle's hardware systems and provides output commands indicative thereof. The vehicle operations programming applications may include adaptive cruise control, automated mayday, and obstacle and collision warning systems, among others. Also disclosed is a new computing architecture that organizes the applications and systems in the vehicle into two groups: driver assistance systems and mobile services and information systems. Also disclosed is a drive recorder that maintains records of the statuses of all vehicle systems and of the driver.

대표청구항 ▼

A computing architecture for a motorized land-based vehicle is disclosed. The computing architecture includes a data network comprised of a plurality of interconnected processors, a first group of sensors responsive to environmental conditions around the vehicle, a second group of sensors responsive

A computing architecture for a motorized land-based vehicle is disclosed. The computing architecture includes a data network comprised of a plurality of interconnected processors, a first group of sensors responsive to environmental conditions around the vehicle, a second group of sensors responsive to the vehicle's hardware systems, and a map database containing data that represent geographic features in the geographic area around the vehicle. A vehicle-environment modeling program, executed on the data network, uses the outputs from the first and second groups of sensors and the map database to provide and continuously update a data model that represents the vehicle and the environmental around the vehicle, including geographic features, conditions, structures, objects and obstacles around the vehicle. Vehicle operations programming applications, executed on the data network, use the data model to determine desired vehicle operation in the context of the vehicle's environment. A driver interface receives the vehicle driver's input. Vehicle control programming, executed on the data network, receives outputs from the vehicle operations programming applications and the driver interface, determines a resolved operation for the vehicle's hardware systems and provides output commands indicative thereof. The vehicle operations programming applications may include adaptive cruise control, automated mayday, and obstacle and collision warning systems, among others. Also disclosed is a new computing architecture that organizes the applications and systems in the vehicle into two groups: driver assistance systems and mobile services and information systems. Also disclosed is a drive recorder that maintains records of the statuses of all vehicle systems and of the driver. located on the underlying track at the position. 4. The method of claim 1 further receiving the force data from at least horizontal, vertical and angle of attack strain gage sensors located on the underlying track at the position. 5. The method of claim 1 further comprising calibrating the sensed position data to known forces applied to the surface of the railhead. 6. A method for measuring the angle of attack in a set of wheels of a rail vehicle on track, the track having a shallow curvature, said method comprising: obtaining angle of attack data for each wheel in the set of wheels on the track having the shallow curvature at a position on the track, sensing position data at the position on the track, the sensed position data calibrated to railhead shape of the track at the position and weight of the rail vehicle at the position, removing cross-talk from the obtained angle of attack data based on the sensed position data, determining an angle of attack value based on the angle of attack data with the cross-talk removed. 7. The method of claim 6 wherein obtaining obtains time sampled data. 8. A method for measuring the dynamic angle of attack for the leading and trailing sets of wheels in trucks of rail vehicles on track, said method comprising: determining raw angles of attack for all sets of wheels, removing cross-talk from the determined raw angles of attack, selecting only those raw angles of attack from the aforesaid step that have trucks on the track within a predetermined range of lateral to vertical force ratios wherein the selected trucks are properly steering trucks, calculating a dynamic angular offset value based on the selected raw angles of attack of the properly steering trucks, subtracting the calculated dynamic angular offset value from the raw angles of attack determined for all sets of wheels to arrive at a dynamic angle of attack for each wheel set. 9. The method of claim 8 wherein determining raw angles of attack further includes: at a first point on the outside rail of the track measuring an outside angle of attack value with a first angle of attack sensor for each outside wheel at the time when the outside wheel passes directly over a first point, at a second point on the inside rail of the track measuring an inside angle of attack value with a second angle of attack sensor for each inside wheel at the time each inside wheel passes directly over a second point, the second point located on a line perpendicular to a line tangent to the outside rail at the first point, measuring the speed of each set of wheels, determining a raw angle of attack for each set of wheels based on the speed and the outside and inside angle of attack values. 10. The method of claim 8 wherein the times when the outside wheel passes directly over the first point and when the inside wheel passes directly over the second point are determined by: taking a derivative of time sampled data from the corresponding angle of attack sensor, locating a peak time of the derivative in a predetermined window of time sampled data to obtain an angle of attack value at said peak time. 11. The method of claim 10 wherein locating a peak uses a polynomial fit process. 12. The method of claim 8 wherein selecting includes: at a point on the outside rail of the track measuring (1) vertical force with a first vertical strain gage, and (2) lateral force with a first lateral strain gage wherein the sensed position data is based on calibrated data for a known force on the railhead, determining a ratio between the measured lateral force and the measured vertical force at the point for the outside wheel of each leading set of wheels in each truck, determining whether the ratio is within a predetermined range and, if so, averaging the raw angles of attack for the trailing wheel connected to the leading wheel with all other trailing wheel raw angles of attack that have corresponding ratios within the predetermined range to obt ain an average angular offset value. 13. The method of claim 12 wherein the predetermined range is less than 0.1. 14. The method of claim 13 wherein the predetermined range also includes when the ratio is between 0.1 and 0.17 and the ratio for trailing wheel divided by the ratio of the leading wheel is less than 0.5. 15. A method for measuring the angle of attack for a set of wheels traveling over track having outside and inside rails, the method comprising: at a first point on the outside rail of the track measuring (1) vertical force with a first vertical sensor, (2) lateral force with a first lateral sensor (3) an outside angle of attack value with a first angle of attack sensor for the outside wheel at the time and (4) an outside position signal with a first position sensor when the outside wheel passes directly over the first point, at a second point on the inside rail of the track measuring (1) vertical force with a second vertical sensor, (2) the lateral force with a second lateral sensor (3) an inside angle of attack value with a second angle of attack sensor for the inside wheel and (4) an inside position signal with a second position sensor at the time the inside wheel passes directly over the second point, the second point located on a line perpendicular to a line tangent to the outside rail at the first point, measuring the speed of each set of wheels, determining a raw angle of attack for the set of wheels based on the speed and the outside and inside angle of attack values, the determined raw angle of attack having cross-talk removed based on the outside and inside position signals, wherein the outside and inside position data is based on calibrated data for known forces on the railhead, determining a ratio between the measured lateral force and the measured vertical force at the first point for the outside wheel, determining whether the ratio is within a predetermined range and, if so, averaging the raw angle of attack with all other raw angles of attack that have corresponding ratios within the predetermined range for other sets of wheels to obtain an average angular offset value, calculating a dynamic angle of attack for the set of wheels by subtracting the average angular offset value from each raw angle of attack to obtain a dynamic angle of attack value for the set of wheels. 16. The method of claim 15 wherein the times when the outside wheel passes directly over the first point and when the inside wheel passes directly over the second point are determined by: taking a derivative of time sampled data from the corresponding angle of attack strain gage, locating a peak time of the derivative so as to obtain an angle of attack value at said peak time. 17. The method of claim 16 wherein the step of locating a peak time uses a polynomial fit process. 18. A method for measuring the raw angle of attack for the leading and trailing sets of wheels in trucks of rail vehicles traveling over track having outside and inside rails, the method comprising: at a first point on the outside rail of the track measuring (1) vertical force with a first vertical sensor, (2) lateral force with a first lateral sensor (3) an outside angle of attack value with a first angle of attack sensor for each outside wheel at the time and (4) outside position signal with a first position sensor when the outside wheel passes directly over the first point, at a second point on the inside rail of the track measuring (1) vertical force with a second vertical sensor, (2) the lateral force with a second lateral sensor and (3) an inside angle of attack value with a second angle of attack sensor for each inside wheel and (4) an inside position signal with a second position sensor at the time each inside wheel passes directly over the second point, the second point located on a line perpendicular to a line tangent to the outside rail at the first point, measuring the speed of each set of wheels, determining a raw angle of attac