Project 2 Adaptive Cruise Control
Adaptive cruise control (ACC) is a system designed to help vehicles maintain a safe following distance and stay within the speed limit. This system adjusts a car's speed automatically so drivers don't have to.
Adaptive cruise control is one of 20 terms used to describe its functions so that you might see adaptive cruise control as the following in advertisements and vehicle descriptions:
- Active cruise control
- Dynamic cruise control
- Radar cruise control
- Automatic cruise control
- Intelligent cruise control
ACC functions by sensory technology installed within vehicles such as cameras, lasers, and radar equipment, which creates an idea of how close one car is to another, or other objects on the roadway. For this reason, ACC is the basis for future car intelligence.
These sensory technologies allow the car to detect and warn the driver about potential forward collisions. When this happens, red lights begin to flash, and the phrase 'brake now!' appears on the dashboard to help the driver slow down. There might also be an audible warning.
Advantages of Adaptive Cruise Control
Some key advantages of adaptive cruise control mentioned by MyCarDoesWhat.org include an increase in road safety, as cars with this technology will keep the adequate spacing between them and other vehicles. These space-mindful features will also help prevent accidents that result from an obstructed view or close following distance. Similarly, ACC will help maximize traffic flow because of its spatial awareness. As a driver, you don't have to worry about your speed, and instead, you can focus on what is going on around you.
Limitations of Adaptive Cruise Control
Although there are many advantages to adaptive cruise control, there are still limitations to consider. One of the main faults in this system is the fact that it is not entirely autonomous. The driver of the vehicle still needs to practice safe driving habits that will work in tandem with this technology to produce the best results. Similarly, adverse weather conditions like snow, rain, or fog might confuse the system's sensors, as well as environmental factors such as driving through tunnels.
Types of Adaptive Cruise Control
Radar-Based Systems
According to eInfoChips, radar-based systems work by placing radar-based sensors on or around plastic fascias to detect your vehicle's surroundings. Each radar sensor works together to create a comprehensive picture of the vehicle's proximity to other cars or potentially hazardous objects. This type of sensor can look different depending on the design and model of the car.
Laser-Based Systems
As mentioned by Electronic Design, this type of ACC system operates out of a large black box typically placed in the grille of your vehicle. It uses laser technology to detect the proximity of objects to your car. It does not operate well during rainstorms and other weather conditions.
Binocular Computer Vision Systems (Optical)
According to ExtremeTech, this is a relatively new ACC system put into use in 2013. It uses small cameras that are placed on the back of a vehicle's rearview mirror to detect front-facing objects.
Assisting Systems
Assisting systems are radar-based add-ons that customers can buy together. These pre-crash systems can offer lane control, brake assistance, cruise control, proximity alerts to objects like corners, and steering power.
Multi-Sensor Systems
According to Fierce Electronics, adaptive cruise control systems sometimes integrate more than one type of sensor to aid in a vehicle's operation. Multi-sensor systems incorporate several different sensor types to provide a driver with advanced information. These sensors might include GPS data equipment or cameras to gather information about a vehicle's geographic environment and proximity to other cars.
Predictive Systems
As mentioned by Autoblog, prediction systems are a type of ACC that uses sensory data to predict the actions of neighboring vehicles. This means that your car might slow down to brace for another vehicle suddenly switching lanes and, in doing so, promotes passenger safety.
Adaptive cruise control is evolving each year. Car companies are continuously making adjustments to this technology and, in doing so, creating more common and affordable options that can be purchased with a new car or added to older car models, making driving safer for everyday people.
Main Project – Adaptive Cruise Control (ACC)
General Overview:
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 which is driving in 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.
THE ABOVE MODEL SHOWS THE ABOVE GIVEN REQUIREMENT STATEMENT GIVEN.
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.
THE ABOVE MODEL SHOWS THE ABOVE GIVEN REQUIREMENT STATEMENT GIVEN.
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
- LeadVehicle_Speed_equal_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)]<="" em=""></set_speed)>
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 |
- |
- |
Steps model design:
- Open the blank Simulink page.
- Open the given model in question.
- Click on the model settings.
- Then go to Solver settings in that following setting to do
- Go to solver section.
- Type to “fixed-step”.
- Solver to “Discrete (no continuous state)”.
- Click on solver details in the change sample time to “0.01sec”.
- Then click on code generation.
- Click on system target file to “embedder coder”
- Then click on the apply and then ok.
- Then click in modeling tab
- In that click on link to data dictionary.
- Click on the link to data dictionary
- Then one popup window open then there will be two options
- Browse file if already having SLDD file if any change then clicks on the browse option.
- if create new SLDD file then choose option “NEW”.
- If creating new “enable model access to base workspace”.
- Then creating new SLDD file choose which file having model there will be saved.
- Then click on “model explore”
- Click on the external data
- Then subfile open in the “I saved SLDD Saved file open”.
- Then which name SLDD file click on right click.
- In given in values in the question values enter in SLDD file
- That will save on design data.
- And also mestion the data type in Chart parameters.
After completion of model design the signal resolve and propagated signal.
- Click on the signal line then right click on properties.
- If signal first time appear the that signal, we resolved and after that next time that signal, we propagated.
- Then we check model will do
- Click on model tab the click on the model advisor.
- Then select whole subsystem to be selected.
- Then unselect all the check only select the model standards for MAB.
- Then click on then run selected checks
- Then errors and warnings show that can be cleared.
- After that we click on generate report.
- Choose the directory path and file name and choose the HTML format.
- Then click ok
- Report generated in HTML type and that folder in main folder saved.
- Embedded code generation.
- Click on the apps and then choose the embedded coder.
- Click on embedded coder
- Click on the build option then
- Embedded code generated.
Requirement tag:
- The given problem requirement can be taken in word sheet.
- Then that word sheet saved in main folder model file.
- Then open the word file.
- Then which block tag the main head line selects.
- Then go to MATLAB window.
- Then choose which subsystem to be tagged.
- Then right click on then choose the requirement.
- Then click on the Link to section work.
- Then model will tag with word docs.
the embedded code attached below.
/*
* File: PROJECTMBD.c
*
* Code generated for Simulink model 'PROJECTMBD'.
*
* Model version : 1.30
* Simulink Coder version : 9.5 (R2021a) 14-Nov-2020
* C/C++ source code generated on : Wed May 18 00:22:36 2022
*
* Target selection: ert.tlc
* Embedded hardware selection: Intel->x86-64 (Windows64)
* Code generation objectives: Unspecified
* Validation result: Not run
*/
#include "PROJECTMBD.h"
#include "PROJECTMBD_private.h"
/* Named constants for Chart: '/Requirement_sub_part' */
#define IN_LeadVehicle_Detected_Follow ((uint8_T)1U)
#define IN_LeadVehicle_Detected_Resume ((uint8_T)2U)
#define IN_LeadVehicle_Not_Detected_Res ((uint8_T)4U)
#define IN_LeadVehicle_Speed_equal_Set_ ((uint8_T)5U)
#define IN_LeadVehicle_Speed_lessthan_S ((uint8_T)6U)
#define PROJECTMBD_IN_ACC_OFF_MODE ((uint8_T)1U)
#define PROJECTMBD_IN_ACC_ON_MODE ((uint8_T)2U)
#define PROJECTMBD_IN_ACC_STANDBY_MODE ((uint8_T)3U)
#define PROJECTMBD_IN_NO_ACTIVE_CHILD ((uint8_T)0U)
#define PRO_IN_LeadVehicle_Not_Detected ((uint8_T)3U)
/* Exported data definition */
/* Definition for custom storage class: ExportToFile */
uint8_T Acceleration_Mode; /* '/Unit Delay' */
/* Block states (default storage) */
DW_PROJECTMBD_T PROJECTMBD_DW;
/* External inputs (root inport signals with default storage) */
ExtU_PROJECTMBD_T PROJECTMBD_U;
/* External outputs (root outports fed by signals with default storage) */
ExtY_PROJECTMBD_T PROJECTMBD_Y;
/* Real-time model */
static RT_MODEL_PROJECTMBD_T PROJECTMBD_M_;
RT_MODEL_PROJECTMBD_T *const PROJECTMBD_M = &PROJECTMBD_M_;
real_T rt_roundd(real_T u)
{
real_T y;
if (fabs(u) < 4.503599627370496E+15) {
if (u >= 0.5) {
y = floor(u + 0.5);
} else if (u > -0.5) {
y = 0.0;
} else {
y = ceil(u - 0.5);
}
} else {
y = u;
}
return y;
}
/* Model step function */
void PROJECTMBD_step(void)
{
/* local block i/o variables */
uint8_T LeadVehicle_Speed;
uint8_T DriveVehicle_Speed;
uint8_T LeadVehicle_Detected;
int32_T tmp;
int32_T tmp_0;
uint8_T tmp_1;
uint8_T tmp_2;
/* Outputs for Atomic SubSystem: '/ACC' */
/* Sum: '/Add' incorporates:
* Inport: '/CameraInput_LeadVehicle'
* Inport: '/RadarInput_LeadVehicle'
*/
LeadVehicle_Speed = (uint8_T)((uint32_T)CameraInput_LeadVehicle +
PROJECTMBD_U.RadarInput_LeadVehicle);
/* UnitDelay: '/Unit Delay' */
Acceleration_Mode = PROJECTMBD_Y.Acceleration_Mode_m;
/* Sum: '/Add2' incorporates:
* Inport: '/CameraInput_DriveVehicle'
* Inport: '/RadarInput_DriveVehicle'
* Sum: '/Add'
*/
DriveVehicle_Speed = (uint8_T)((uint32_T)(uint8_T)((uint32_T)
CameraInput_DriveVehicle + Acceleration_Mode) + RadarInput_DriveVehicle);
/* SignalConversion: '/Signal Conversion' incorporates:
* Inport: '/RadarInput_DriveVehicle'
*/
LeadVehicle_Detected = RadarInput_DriveVehicle;
/* Chart: '/Requirement_sub_part' incorporates:
* Inport: '/CruiseSwitch'
* Inport: '/Set_Gap'
* Inport: '/Set_Speed'
* Inport: '/Time_Gap'
* UnitDelay: '/Unit Delay'
*/
if (PROJECTMBD_DW.is_active_c3_PROJECTMBD == 0U) {
PROJECTMBD_DW.is_active_c3_PROJECTMBD = 1U;
PROJECTMBD_DW.is_c3_PROJECTMBD = PROJECTMBD_IN_ACC_OFF_MODE;
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
} else {
switch (PROJECTMBD_DW.is_c3_PROJECTMBD) {
case PROJECTMBD_IN_ACC_OFF_MODE:
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
if (CruiseSwitch) {
PROJECTMBD_DW.is_c3_PROJECTMBD = PROJECTMBD_IN_ACC_STANDBY_MODE;
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
}
break;
case PROJECTMBD_IN_ACC_ON_MODE:
if (Set_Speed == 0) {
PROJECTMBD_DW.is_ACC_ON_MODE = PROJECTMBD_IN_NO_ACTIVE_CHILD;
PROJECTMBD_DW.is_c3_PROJECTMBD = PROJECTMBD_IN_ACC_STANDBY_MODE;
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
} else if (!CruiseSwitch) {
PROJECTMBD_DW.is_ACC_ON_MODE = PROJECTMBD_IN_NO_ACTIVE_CHILD;
PROJECTMBD_DW.is_c3_PROJECTMBD = PROJECTMBD_IN_ACC_OFF_MODE;
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
} else {
switch (PROJECTMBD_DW.is_ACC_ON_MODE) {
case IN_LeadVehicle_Detected_Follow:
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
if (LeadVehicle_Detected == 0) {
PROJECTMBD_DW.is_ACC_ON_MODE = PRO_IN_LeadVehicle_Not_Detected;
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
} else if (((LeadVehicle_Detected == 1) && (LeadVehicle_Speed <
Set_Speed)) || (Time_Gap < Set_Gap)) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Speed_lessthan_S;
PROJECTMBD_Y.Acceleration_Mode_m = 4U;
}
break;
case IN_LeadVehicle_Detected_Resume:
PROJECTMBD_Y.Acceleration_Mode_m = 3U;
if ((DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >=
Set_Speed) && (Time_Gap >= Set_Gap)) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Detected_Follow;
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
} else if (LeadVehicle_Detected == 0) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Not_Detected_Res;
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
} else if ((DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed >
DriveVehicle_Speed) && (Time_Gap >= Set_Gap)) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Speed_equal_Set_;
PROJECTMBD_Y.Acceleration_Mode_m = 5U;
}
break;
case PRO_IN_LeadVehicle_Not_Detected:
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
if ((LeadVehicle_Detected == 1) && (DriveVehicle_Speed == Set_Speed) &&
(LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Detected_Follow;
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
} else if (((LeadVehicle_Detected == 1) && (LeadVehicle_Speed <
Set_Speed)) || (Time_Gap < Set_Gap)) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Speed_lessthan_S;
PROJECTMBD_Y.Acceleration_Mode_m = 4U;
}
break;
case IN_LeadVehicle_Not_Detected_Res:
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
break;
case IN_LeadVehicle_Speed_equal_Set_:
PROJECTMBD_Y.Acceleration_Mode_m = 5U;
if ((LeadVehicle_Detected == 0) || (DriveVehicle_Speed <= Set_Speed))
{
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Not_Detected_Res;
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
} else if (((LeadVehicle_Speed < Set_Speed) && (LeadVehicle_Speed <
DriveVehicle_Speed)) || ((int32_T)rt_roundd(0.75 *
(real_T)Set_Gap) == Time_Gap)) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Speed_lessthan_S;
PROJECTMBD_Y.Acceleration_Mode_m = 4U;
} else if (((DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed >
DriveVehicle_Speed)) || (Time_Gap >= Set_Gap)) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Detected_Resume;
PROJECTMBD_Y.Acceleration_Mode_m = 3U;
}
break;
default:
/* case IN_LeadVehicle_Speed_lessthan_Set_Speed: */
PROJECTMBD_Y.Acceleration_Mode_m = 4U;
if ((LeadVehicle_Detected == 0) && (DriveVehicle_Speed == Set_Speed))
{
PROJECTMBD_DW.is_ACC_ON_MODE = PRO_IN_LeadVehicle_Not_Detected;
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
} else {
tmp = (int32_T)rt_roundd((real_T)LeadVehicle_Speed * 1.25);
tmp_0 = (int32_T)rt_roundd(1.25 * (real_T)Set_Gap);
if (tmp < 256) {
tmp_1 = (uint8_T)tmp;
} else {
tmp_1 = MAX_uint8_T;
}
if (tmp_0 < 256) {
tmp_2 = (uint8_T)tmp_0;
} else {
tmp_2 = MAX_uint8_T;
}
if ((tmp_1 >= DriveVehicle_Speed) && ((int32_T)rt_roundd((real_T)
LeadVehicle_Speed * 0.75) <= DriveVehicle_Speed) &&
(DriveVehicle_Speed < Set_Speed) && (Time_Gap <= tmp_2) &&
(Time_Gap >= (int32_T)rt_roundd(0.75 * (real_T)Set_Gap))) {
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Speed_equal_Set_;
PROJECTMBD_Y.Acceleration_Mode_m = 5U;
}
}
break;
}
}
break;
default:
/* case IN_ACC_STANDBY_MODE: */
PROJECTMBD_Y.Acceleration_Mode_m = 1U;
if (!CruiseSwitch) {
PROJECTMBD_DW.is_c3_PROJECTMBD = PROJECTMBD_IN_ACC_OFF_MODE;
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
} else if (Set_Speed == 1) {
PROJECTMBD_DW.is_c3_PROJECTMBD = PROJECTMBD_IN_ACC_ON_MODE;
PROJECTMBD_DW.is_ACC_ON_MODE = IN_LeadVehicle_Detected_Follow;
PROJECTMBD_Y.Acceleration_Mode_m = 0U;
}
break;
}
}
/* End of Chart: '/Requirement_sub_part' */
/* End of Outputs for SubSystem: '/ACC' */
}
/* Model initialize function */
void PROJECTMBD_initialize(void)
{
/* (no initialization code required) */
}
/* Model terminate function */
void PROJECTMBD_terminate(void)
{
/* (no terminate code required) */
}
/*
* File trailer for generated code.
*
* [EOF]
*/