SYSTEM AND METHOD FOR MODELING ADVANCED AUTOMOTIVE SAFETY SYSTEMS
A system and methods are disclosed for providing an integrated software development environment for the design, verification, and validation of advanced automotive safety systems. The system allows automotive software to be developed on a host computer using a collection of computer programs running simultaneously as processes and synchronized by a central process. The software disclosed uses separate synchronized processes, permitting signals from disparate sources to be generated by a simulation running on the host computer or from actual sensors and data bus signals coming from and going to actual vehicle hardware which is connected to their bus counterparts in the host computer on a real-time basis. The methods provide a data model that first extends the capabilities of the physical data model and then translates, gates, optimizes, fuses, filters, and manages the physical representation of the logical model into a state estimation of the situation around the vehicle.
1 . A sensor system, comprising:
a plurality of object detection sensors;
a memory;
a controller connected to the object detection sensors, wherein the controller is configured to:
receive sensor reports from the object detection sensors, wherein the sensor reports include time of detection,
determine different mean values from the sensor reports, wherein each of the different mean values is an average value of a central tendency of a one-sigma probability error distribution of a corresponding sensor report in units of cross range and down range values, wherein the one-sigma probability error distribution of a corresponding object detection sensor represents measurement errors and multiple variance values of the detected object,
store the sensor reports and the different mean values in memory as stored values,
retrieve the stored values from memory,
determine a covariance value of a detected object using the retrieved stored values,
use a first measured value of the detected object at a first time of detection, the determined covariance value, and a recursive process to make a first estimate of state for the detected object, wherein the first estimate of state includes at least one of position, velocity, and acceleration,
store the first estimate of state and determined covariance value in memory,
use the recursive process to receive a second measured value of the detected object at a second time of detection,
compare the first estimate of state to the second measured value and store the difference in memory.
2 . The system of claim 1 , wherein the object detection sensors include emitting and non-emitting non-contact sensors.
3 . The system of claim 1 , wherein data structure of the sensor reports includes latency of the report.
4 . The system of claim 1 , wherein the object detection sensor reports comprise one or more object attributes of the detected object.
5 . The system of claim 4 , wherein the one or more object attributes comprise kinematic state of the detected object.
6 . The system of claim 1 , wherein the structure of the system is a variable structure using interacting multiple models.
7 . The system of claim 1 , wherein the system uses one of a track oriented or a measurement oriented data association process.
8 . The system of claim 1 , wherein the memory comprises any hard disk, Read Only Memory (ROM), Dynamic Random Access (RAM) memory, or any combination of different memory devices.
9 . The system of claim 1 , wherein the controller comprises at least one of a processor, a micro-controller, and a programmable logic device.
10 . The system of claim 1 , wherein the object detection sensors are mounted to a platform.
11 . A state estimation method, comprising:
mounting a plurality of object detection sensors to a platform;
configuring a controller connected to the object detection sensors to:
receive sensor reports from the object detection sensors, wherein the sensor reports include time of detection,
determine different mean values from the sensor reports, wherein each of the different mean values is an average value of a central tendency of a one-sigma probability error distribution of a corresponding sensor report in units of cross range and down range values, wherein the one-sigma probability error distribution of a corresponding object detection sensor represents measurement errors and multiple variance values of the detected object,
store the sensor reports and the different mean values in memory as stored values,
retrieve the stored values from memory,
determine a covariance value of a detected object using the retrieved stored values,
use a first measured value of the detected object at a first time of detection, the determined covariance value, and a recursive process to make a first estimate of state for the detected object, wherein the first estimate of state includes at least one of position, velocity, and acceleration,
store the first estimate of state and determined covariance value in memory,
use the recursive process to receive a second measured value of the detected object at a second time of detection,
compare the first estimate of state to the second measured value and store the difference in memory.
12 . The method of claim 11 , wherein the object detection sensors include emitting and non-emitting non-contact sensors.
13 . The method of claim 11 , wherein data structure of the sensor reports includes latency of the report.
14 . The method of claim 11 , wherein the object detection sensor reports comprise one or more object attributes of the detected object.
15 . The method of claim 14 , wherein the one or more object attributes comprise kinematic state of the detected object.
16 . The method of claim 11 , wherein the structure of the system is a variable structure using interacting multiple models.
17 . The method of claim 11 , wherein the system uses one of a track oriented or a measurement oriented data association process.
18 . The method of claim 11 , wherein the memory comprises any hard disk, Read Only Memory (ROM), Dynamic Random Access (RAM) memory, or any combination of different memory devices.
19 . The method of claim 11 , wherein the controller comprises at least one of a processor, a micro-controller, and a programmable logic device.
20 . The method of claim 11 , wherein the platform is mounted to a vehicle.