Project 2 Adaptive Cruise Control
Aim: To build an Advanced Driver assistant system as per the given requirement.
ADAS:
Adaptive Cruise Control Feature for passenger cars allows the host vehicle to adapt to the speed in line with the flow of traffic. Driving in heavy traffic or keeping a safe distance to the preceding vehicle calls for a high level of concentration. The Adaptive Cruise Control feature can reduce the stress on the driver by automatically controlling the vehicle speed & maintaining a predefined minimum distance to the preceding vehicle. As a consequence, the driver enjoys more comfort & can concentrate on the road little better.
A radar sensor is usually at the core of the Adaptive Cruise Control. Installed at the front of the vehicle, the system permanently monitors the road ahead. As long as the road ahead is clear, cruise control feature maintains the speed set by the driver. If the system spots a slower vehicle within its detection range, it gently reduces speed by releasing the accelerator or actively engaging the brake control system. If the vehicle ahead speeds up or changes lanes, the cruise control automatically accelerates to the driver’s desired speed.
Standard Adaptive Cruise Control can be activated from speeds of around 30 km/h (20 mph) upwards and supports the driver, primarily on cross-country journeys or on freeways. The cruise control stop & go variant is also active at speeds below 30 km/h (20 mph). It can maintain the set distance to the preceding vehicle even at very low speeds and can decelerate to a complete standstill. When the vehicle remains stopped longer, the driver needs only to reactivate the system, for example by briefly stepping on the gas pedal to return to cruise control mode. In this way, cruise control stop & go supports the driver even in heavy traffic and traffic jams.
Since Adaptive Cruise Control is a comfort and convenience system, brake interventions and vehicle acceleration only take place within defined limits. Even with Adaptive Cruise Control switched on, it remains the driver’s responsibility to monitor the speed and distance from the vehicle in front.
Objective of Main Project:
- Developing Adaptive Cruise Control feature as per the Requirement Document using MATLAB Simulink.
- Follow all the MBD related processes: Requirement Tagging & Traceability, SLDD creation, Configuration Parameter changes, Model Advisor check & Code Generation.
- In Configuration Parameters: enable “Support Floating Numbers” under Code Generation settings.
- Use Embedded Coder to generate the code.
- If choosing code generation, Storage class for Input signals: ImportedExtern; Storage class for Output signal: Export to File; Storage class for local signals: localizable; Storage class for calibration signals: Const.
- Choose sample time for all signals as 0.01s
Requirement 1– Lead Vehicle:
- Lead Vehicle is a vehicle that is driving on the road ahead of our drive vehicle. Two input signals (Signal Name: CameraInput_LeadVehicle & RadarInput_LeadVehicle).
- Ideally sensor fusion techniques will be deployed to process & analyze data from camera & radar. For complexity reasons, let’s not adapt to any such algorithms.
- We can simply add both the radar & camera inputs & the corresponding output is read as Speed profile output (Signal Name: LeadVehicle_Speed).
- Speed data of the lead vehicle is critical in implementing the Adaptive Cruise Control algorithm.
Requirement 2 – Drive Vehicle:
- Drive Vehicle is the vehicle driven by the user & this is the vehicle which has ACC algorithm in it.
- Like the Lead Vehicle, Drive Vehicle algorithm also has 2 input signals (Signal Name: CameraInput_DriveVehicle, RadarInput_DriveVehicle) & one signal coming as an Input to this subsystem (Signal Name: Acceleration_Mode) – three inputs into this requirement in total.
- Like the above requirement, sensor fusion techniques will also be deployed here, for complexity reasons we are ignoring them.
- Two output signals come from this subsystem (Signal Name: DriveVehicle_Speed & LeadVehicle_Detected).
- Signal DriveVehicle_Speed is summation of three input signals mentioned above & LeadVehicle_Detected is renamed from Input Signal RadarInput_DriveVehicle by mere use of Signal Conversion block.
Requirement 3 – Adaptive Cruise Control Algorithm:
- Adaptive Cruise Control feature has 3 major modes of operation: OFF Mode, STANDBY Mode & ON Mode. This particular requirement has to be implemented as state machine logic in Simulink.
- The input signals to this state machine system are (Signal Name: Time_Gap, Set_Speed, Set_Gap, CruiseSwitch, SetSwitch).
- Also, the output signals (Signal Name: DriveVehicle_Speed & LeadVehicle_Detected) from requirement-2 is fed back as an input signal into this state machine block.
- Additionally, output signal (Signal Name: LeadVehicle_Speed) from requirement-1 is given as an input signal to this state machine block as well.
- Output from this subsystem is a signal (Signal Name: Acceleration_Mode) which governs the vehicular speed of the drive vehicle which automatically adjusts its speed & velocity to match the lead vehicle.
Requirement 3a – ACC OFF MODE state logic:
- This is the default state inside state machine logic. Output signal Acceleration_Mode is at value 0 in this state.
- This state is governed by input signal CruiseSwitch.
- If CruiseSwitch is equal to 1, state ACC STANDBY mode will get activated. If CruiseSwitch is equal to 0, state ACC OFF mode will get activated, from either ACC ON mode or ACC STANDBY mode
Requirement 3b – ACC STANDBY MODE state logic:
- This is the second activated state inside state machine logic. Output signal Acceleration_Mode is at value 1 in this state.
- This state is governed by both input signals CruiseSwitch & SetSwitch.
- If CruiseSwitch is equal to 1, state ACC STANDBY mode will get activated. If CruiseSwitch is equal to 0, state ACC OFF mode will get activated, from either ACC ON mode or ACC STANDBY mode
- If SetSwitch is equal to 1, state ACC ON mode will get activated. If SetSwitch is equal to 0, state ACC STANDBY mode will get activated.
Requirement 3c – ACC ON MODE state logic:
This state will be activated when input signal SetSwitch is equal to 1. There are 6 sub states to this state logic: They are:
- LeadVehicle_Detected_Follow (Default)
- LeadVehicle_Not_Detected
- LeadVehicle_Detected_Resume
- LeadVehicle_Not_Detected_Resume
- LeadVehicle_Speed_lessthan_Set_Speed
Requirement 3c (i) – LeadVehicle_Detected_Follow (ACC ON MODE):
- This is the default sub state inside ACC ON MODE state. Output signal Acceleration_Mode is equal to 2.
- Condition to transit from LeadVehicle_Detected_Follow to LeadVehicle_Not_Detected; Input signal condition LeadVehicle_Detected == 0.
- Condition to transit from LeadVehicle_Detected_Follow to LeadVehicle_Speed_lessthan_Set_Speed; Input Signals condition (LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed) || (Time_Gap < Set_Gap).
Requirement 3c (ii) – LeadVehicle_Not_Detected (ACC ON MODE):
- Output signal Acceleration_Mode is equal to 1.
- Condition to transit from LeadVehicle_Not_Detected to LeadVehicle_Detected_Follow; Input signals condition [(LeadVehicle_Detected==1) && (DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)]
- Condition to transit from LeadVehicle_Not_Detected to LeadVehicle_Speed_lessthan_Set_Speed; Input signals condition [(LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed) || (Time_Gap < Set_Gap)]
Requirement 3c (iii) – LeadVehicle_Detected_Resume (ACC ON MODE):
- Output signal Acceleration_Mode is equal to 3.
- Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Detected_Follow; Input signals condition [(DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)]
- Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Not_Detected_Resume; Input signal condition LeadVehicle_Detected==0.
- Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Speed_equal_Set_Speed; Input Signal condition [(DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed > DriveVehicle_Speed) && (Time_Gap >= Set_Gap)]
Requirement 3c (iv) - LeadVehicle_Not_Detected_Resume (ACC ON MODE):
- Output signal Acceleration_Mode is equal to 1.
Requirement 3c (v) - LeadVehicle_Speed_lessthan_Set_Speed (ACC ON MODE):
- Output signal Acceleration_Mode is equal to 4.
- Condition to transit from LeadVehicle_Speed_lessthan_Set_Speed to LeadVehicle_Not_Detected; Input signal condition [(LeadVehicle_Detected == 0) && (DriveVehicle_Speed == Set_Speed)]
- Condition to transit from LeadVehicle_Speed_lessthan_Set_Speed to LeadVehicle_Speed_equal_Set_Speed; Input signals condition [((LeadVehicle_Speed*1.25>=DriveVehicle_Speed) && (LeadVehicle_Speed * 0.75<=DriveVehicle_Speed)) && (DriveVehicle_Speed < Set_Speed) && ((Time_Gap<=1.25*Set_Gap) && (Time_Gap >=0.75*Set_Gap))]
Requirement 3c (vi) - LeadVehicle_Speed_equal_Set_Speed (ACC ON MODE):
- Output signal Acceleration_Mode is equal to 5.
- Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Not_Detected_Resume; Input signal conditions is [(LeadVehicle_Detected == 0) || (DriveVehicle_Speed <= Set_Speed)]
- Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Detected_Resume; Input signal conditions [(DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed > DriveVehicle_Speed) || (Time_Gap >= Set_Gap)]
- Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Speed_lessthan_Set_Speed; Input signals conditions [(LeadVehicle_Speed<Set_Speed) && (LeadVehicle_Speed<DriveVehicle_Speed) || (Time_Gap == 0.75*Set_Gap)]
Final Model:-
The model is created, we need to create the Simulink data dictionary(Sldd) for the respective file.
To create the sldd we need to open the modelling tab in the simulink.
In the Modelling tab we need to select link to data dictionary and select new.
Signals & Calibration Data List:
|
Signal / Calibration Name |
Signal Type |
Data Type |
Dimension |
Min |
Max |
Initial Value |
Units |
|
CameraInput_LeadVehicle |
Input |
Uint8 |
1 |
0 |
255 |
- |
- |
|
RadarInput_LeadVehicle |
Input |
Uint8 |
1 |
0 |
255 |
- |
- |
|
CameraInput_DriveVehicle |
Input |
Uint8 |
1 |
0 |
255 |
- |
- |
|
RadarInput_DriveVehicle |
Input |
Uint8 |
1 |
0 |
255 |
- |
- |
|
Time_Gap |
Input |
Uint8 |
1 |
0 |
255 |
- |
- |
|
Set_Speed |
Input |
Uint8 |
1 |
0 |
255 |
- |
- |
|
Set_Gap |
Input |
Uint8 |
1 |
0 |
255 |
- |
- |
|
CruiseSwitch |
Input |
Boolean |
1 |
0 |
1 |
- |
- |
|
SetSwitch |
Input |
Boolean |
1 |
0 |
1 |
- |
- |
|
Acceleration_Mode |
Output |
Uint8 |
1 |
0 |
255 |
- |
- |
|
LeadVehicle_Speed |
Output |
Uint8 |
1 |
0 |
255 |
- |
- |
|
DriveVehicle_Speed |
Output |
Uint8 |
1 |
0 |
255 |
- |
- |
|
LeadVehicle_Detected |
Output |
Uint8 |
1 |
0 |
255 |
- |
- |
This are the input for the SLDD.
After creation of SLDD resolve the signals in the model and link it to SLDD.
Now do the require meant tagging from the word file to the requirement 1 and 2.
Select the word text for the requirement, go to matlab,right click on the subsystem and in requirements select the link to selection in word option.
After requirement tagging, do the configuration settings.
Step size of 0.01s.
In code generation setting select ert.tlc(embedded coder).
In coverage setting select MCDC coverage.
Run the model and do the model advisor check for the MAAB guidelines.
After Model Advisor do generate the code for the model.
/* Sum: '<S2>/Add' incorporates:
* Inport: '<Root>/CameraInput_LeadVehicle'
* Inport: '<Root>/RadarInput_LeadVehicle'
*/
rtb_Add = ADAS_Final_Model_U.CameraInput_LeadVehicle +
ADAS_Final_Model_U.RadarInput_LeadVehicle;
/* Chart: '<Root>/Chart' incorporates:
* Inport: '<Root>/CruiseSwitch'
* Inport: '<Root>/RadarInput_DriveVehicle'
* Inport: '<Root>/SetSwitch'
* Inport: '<Root>/Set_Gap'
* Inport: '<Root>/Set_Speed'
* Inport: '<Root>/Time_Gap'
*/
if (ADAS_Final_Model_DW.is_active_c3_ADAS_Final_Model == 0U) {
ADAS_Final_Model_DW.is_active_c3_ADAS_Final_Model = 1U;
ADAS_Final_Model_DW.is_c3_ADAS_Final_Model =
ADAS_Final_Model_IN_AC_OFF_MODE;
ADAS_Final_Model_B.Acceleration_Mode = 0.0;
} else {
switch (ADAS_Final_Model_DW.is_c3_ADAS_Final_Model) {
case ADAS_Final_Model_IN_ACC_ON_MODE:
if (ADAS_Final_Model_U.SetSwitch == 0.0) {
ADAS_Final_Model_DW.is_ACC_ON_MODE = ADAS_Final_M_IN_NO_ACTIVE_CHILD;
ADAS_Final_Model_DW.is_c3_ADAS_Final_Model =
ADAS_Final__IN_ACC_STANDBY_MODE;
ADAS_Final_Model_B.Acceleration_Mode = 1.0;
} else {
switch (ADAS_Final_Model_DW.is_ACC_ON_MODE) {
case IN_LeadVehicle_Detected_Follow:
ADAS_Final_Model_B.Acceleration_Mode = 2.0;
if (ADAS_Final_Model_U.RadarInput_DriveVehicle == 0.0) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
ADA_IN_LeadVehicle_Not_Detected;
ADAS_Final_Model_B.Acceleration_Mode = 0.0;
} else if (((ADAS_Final_Model_U.RadarInput_DriveVehicle == 1.0) &&
(rtb_Add < ADAS_Final_Model_U.Set_Speed)) ||
(ADAS_Final_Model_U.Time_Gap < ADAS_Final_Model_U.Set_Gap))
{
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Speed_lessthan_S;
ADAS_Final_Model_B.Acceleration_Mode = 4.0;
}
break;
case IN_LeadVehicle_Detected_Resume:
ADAS_Final_Model_B.Acceleration_Mode = 3.0;
if ((Sum == ADAS_Final_Model_U.Set_Speed) && (rtb_Add >=
ADAS_Final_Model_U.Set_Speed) && (ADAS_Final_Model_U.Time_Gap >=
ADAS_Final_Model_U.Set_Gap)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Detected_Follow;
ADAS_Final_Model_B.Acceleration_Mode = 2.0;
} else if (ADAS_Final_Model_U.RadarInput_DriveVehicle == 0.0) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Not_Detected_Res;
ADAS_Final_Model_B.Acceleration_Mode = 1.0;
} else if ((Sum < ADAS_Final_Model_U.Set_Speed) && (rtb_Add > Sum) &&
(ADAS_Final_Model_U.Time_Gap >=
ADAS_Final_Model_U.Set_Gap)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Speed_equal_Set_;
ADAS_Final_Model_B.Acceleration_Mode = 5.0;
}
break;
case ADA_IN_LeadVehicle_Not_Detected:
ADAS_Final_Model_B.Acceleration_Mode = 0.0;
if ((ADAS_Final_Model_U.RadarInput_DriveVehicle == 1.0) && (Sum ==
ADAS_Final_Model_U.Set_Speed) && (rtb_Add >=
ADAS_Final_Model_U.Set_Speed) && (ADAS_Final_Model_U.Time_Gap >=
ADAS_Final_Model_U.Set_Gap)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Detected_Follow;
ADAS_Final_Model_B.Acceleration_Mode = 2.0;
} else if (((ADAS_Final_Model_U.RadarInput_DriveVehicle == 1.0) &&
(rtb_Add < ADAS_Final_Model_U.Set_Speed)) ||
(ADAS_Final_Model_U.Time_Gap < ADAS_Final_Model_U.Set_Gap))
{
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Speed_lessthan_S;
ADAS_Final_Model_B.Acceleration_Mode = 4.0;
}
break;
case IN_LeadVehicle_Not_Detected_Res:
ADAS_Final_Model_B.Acceleration_Mode = 1.0;
break;
case IN_LeadVehicle_Speed_equal_Set_:
ADAS_Final_Model_B.Acceleration_Mode = 5.0;
if (((Sum < ADAS_Final_Model_U.Set_Speed) && (rtb_Add > Sum)) ||
(ADAS_Final_Model_U.Time_Gap >= ADAS_Final_Model_U.Set_Gap)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Detected_Resume;
ADAS_Final_Model_B.Acceleration_Mode = 3.0;
} else if ((ADAS_Final_Model_U.RadarInput_DriveVehicle == 0.0) ||
(Sum <= ADAS_Final_Model_U.Set_Speed)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Not_Detected_Res;
ADAS_Final_Model_B.Acceleration_Mode = 1.0;
} else if (((rtb_Add < ADAS_Final_Model_U.Set_Speed) && (rtb_Add <
Sum)) || (0.75 * ADAS_Final_Model_U.Set_Gap ==
ADAS_Final_Model_U.Time_Gap)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Speed_lessthan_S;
ADAS_Final_Model_B.Acceleration_Mode = 4.0;
}
break;
default:
/* case IN_LeadVehicle_Speed_lessthan_Set_Speed: */
ADAS_Final_Model_B.Acceleration_Mode = 4.0;
if ((ADAS_Final_Model_U.RadarInput_DriveVehicle == 0.0) && (Sum ==
ADAS_Final_Model_U.Set_Speed)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
ADA_IN_LeadVehicle_Not_Detected;
ADAS_Final_Model_B.Acceleration_Mode = 0.0;
} else if ((rtb_Add * 1.25 >= Sum) && (rtb_Add * 0.75 <= Sum) &&
(Sum < ADAS_Final_Model_U.Set_Speed) &&
(ADAS_Final_Model_U.Time_Gap <= 1.25 *
ADAS_Final_Model_U.Set_Gap) &&
(ADAS_Final_Model_U.Time_Gap >= 0.75 *
ADAS_Final_Model_U.Set_Gap)) {
ADAS_Final_Model_DW.is_ACC_ON_MODE =
IN_LeadVehicle_Speed_equal_Set_;
ADAS_Final_Model_B.Acceleration_Mode = 5.0;
}
break;
}
}
break;
case ADAS_Final__IN_ACC_STANDBY_MODE:
ADAS_Final_Model_B.Acceleration_Mode = 1.0;
if (ADAS_Final_Model_U.CruiseSwitch == 0.0) {
ADAS_Final_Model_DW.is_c3_ADAS_Final_Model =
ADAS_Final_Model_IN_AC_OFF_MODE;
ADAS_Final_Model_B.Acceleration_Mode = 0.0;
} else if (ADAS_Final_Model_U.SetSwitch == 1.0) {
ADAS_Final_Model_DW.is_c3_ADAS_Final_Model =
ADAS_Final_Model_IN_ACC_ON_MODE;
ADAS_Final_Model_DW.is_ACC_ON_MODE = IN_LeadVehicle_Detected_Follow;
ADAS_Final_Model_B.Acceleration_Mode = 2.0;
}
break;
default:
/* case IN_AC_OFF_MODE: */
ADAS_Final_Model_B.Acceleration_Mode = 0.0;
if (ADAS_Final_Model_U.CruiseSwitch == 1.0) {
ADAS_Final_Model_DW.is_c3_ADAS_Final_Model =
ADAS_Final__IN_ACC_STANDBY_MODE;
ADAS_Final_Model_B.Acceleration_Mode = 1.0;
}
break;
}
}
/* End of Chart: '<Root>/Chart' */
}
if (rtmIsMajorTimeStep(ADAS_Final_Model_M)) {
rt_ertODEUpdateContinuousStates(&ADAS_Final_Model_M->solverInfo);
/* Update absolute time for base rate */
/* The "clockTick0" counts the number of times the code of this task has
* been executed. The absolute time is the multiplication of "clockTick0"
* and "Timing.stepSize0". Size of "clockTick0" ensures timer will not
* overflow during the application lifespan selected.
*/
++ADAS_Final_Model_M->Timing.clockTick0;
ADAS_Final_Model_M->Timing.t[0] = rtsiGetSolverStopTime
(&ADAS_Final_Model_M->solverInfo);
{
/* Update absolute timer for sample time: [0.01s, 0.0s] */
/* The "clockTick1" counts the number of times the code of this task has
* been executed. The resolution of this integer timer is 0.01, which is the step size
* of the task. Size of "clockTick1" ensures timer will not overflow during the
* application lifespan selected.
*/
ADAS_Final_Model_M->Timing.clockTick1++;
}
} /* end MajorTimeStep */
}
/* Derivatives for root system: '<Root>' */
void ADAS_Final_Model_derivatives(void)
{
XDot_ADAS_Final_Model_T *_rtXdot;
_rtXdot = ((XDot_ADAS_Final_Model_T *) ADAS_Final_Model_M->derivs);
/* Derivatives for Integrator: '<Root>/Integrator' */
_rtXdot->Integrator_CSTATE = ADAS_Final_Model_B.Acceleration_Mode;
}
/* Model initialize function */
void ADAS_Final_Model_initialize(void)
{
/* Registration code */
{
/* Setup solver object */
rtsiSetSimTimeStepPtr(&ADAS_Final_Model_M->solverInfo,
&ADAS_Final_Model_M->Timing.simTimeStep);
rtsiSetTPtr(&ADAS_Final_Model_M->solverInfo, &rtmGetTPtr(ADAS_Final_Model_M));
rtsiSetStepSizePtr(&ADAS_Final_Model_M->solverInfo,
&ADAS_Final_Model_M->Timing.stepSize0);
rtsiSetdXPtr(&ADAS_Final_Model_M->solverInfo, &ADAS_Final_Model_M->derivs);
rtsiSetContStatesPtr(&ADAS_Final_Model_M->solverInfo, (real_T **)
&ADAS_Final_Model_M->contStates);
rtsiSetNumContStatesPtr(&ADAS_Final_Model_M->solverInfo,
&ADAS_Final_Model_M->Sizes.numContStates);
rtsiSetNumPeriodicContStatesPtr(&ADAS_Final_Model_M->solverInfo,
&ADAS_Final_Model_M->Sizes.numPeriodicContStates);
rtsiSetPeriodicContStateIndicesPtr(&ADAS_Final_Model_M->solverInfo,
&ADAS_Final_Model_M->periodicContStateIndices);
rtsiSetPeriodicContStateRangesPtr(&ADAS_Final_Model_M->solverInfo,
&ADAS_Final_Model_M->periodicContStateRanges);
rtsiSetErrorStatusPtr(&ADAS_Final_Model_M->solverInfo, (&rtmGetErrorStatus
(ADAS_Final_Model_M)));
rtsiSetRTModelPtr(&ADAS_Final_Model_M->solverInfo, ADAS_Final_Model_M);
}
rtsiSetSimTimeStep(&ADAS_Final_Model_M->solverInfo, MAJOR_TIME_STEP);
ADAS_Final_Model_M->intgData.y = ADAS_Final_Model_M->odeY;
ADAS_Final_Model_M->intgData.f[0] = ADAS_Final_Model_M->odeF[0];
ADAS_Final_Model_M->intgData.f[1] = ADAS_Final_Model_M->odeF[1];
ADAS_Final_Model_M->intgData.f[2] = ADAS_Final_Model_M->odeF[2];
ADAS_Final_Model_M->contStates = ((X_ADAS_Final_Model_T *) &ADAS_Final_Model_X);
rtsiSetSolverData(&ADAS_Final_Model_M->solverInfo, (void *)
&ADAS_Final_Model_M->intgData);
rtsiSetIsMinorTimeStepWithModeChange(&ADAS_Final_Model_M->solverInfo, false);
rtsiSetSolverName(&ADAS_Final_Model_M->solverInfo,"ode3");
rtmSetTPtr(ADAS_Final_Model_M, &ADAS_Final_Model_M->Timing.tArray[0]);
ADAS_Final_Model_M->Timing.stepSize0 = 0.01;
/* InitializeConditions for Integrator: '<Root>/Integrator' */
ADAS_Final_Model_X.Integrator_CSTATE = 0.0;
}
/* Model terminate function */
void ADAS_Final_Model_terminate(void)
{
/* (no terminate code required) */
}
/*
* File trailer for generated code.
*
* [EOF]
*/
Result: With this step, the ADAS model was successfully Built.