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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.…
Chandrakumar ADEPU
updated on 02 Oct 2022
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 a 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, the 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 stops & 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 stops & 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.
The objective of the Main Project:
Requirement 1– Lead Vehicle:
Requirement 2 – Drive Vehicle:
Requirement 3 – Adaptive Cruise Control Algorithm:
Requirement 3a – ACC OFF MODE state logic:
Requirement 3b – ACC STANDBY MODE state logic:
Requirement 3c – ACC ON MODE state logic:
This state will be activated when the input signal SetSwitch is equal to 1. There are 6 sub-states to this state logic: They are:
Requirement 3c (i) – LeadVehicle_Detected_Follow (ACC ON MODE):
Requirement 3c (ii) – LeadVehicle_Not_Detected (ACC ON MODE):
Requirement 3c (iii) – LeadVehicle_Detected_Resume (ACC ON MODE):
Requirement 3c (iv) - LeadVehicle_Not_Detected_Resume (ACC ON MODE):
Requirement 3c (v) - LeadVehicle_Speed_lessthan_Set_Speed (ACC ON MODE):
Requirement 3c (vi) - LeadVehicle_Speed_equal_Set_Speed (ACC ON MODE):
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 |
- |
- |
1. Procedure:
As per the Requirement document, implemented the Adaptive Cruise Control feature.
Lead vehicle
• To detect the lead vehicle speed, simply added both the radar & camera inputs & the corresponding output is read as Speed profile output.
Drive Vehicle
To regulate the "Drive Vehicle speed, as per the Requirement Document added Cameralnput. Radarinput & Acceleration Mode feedback from ACC Algorithm.
• To detect the Lead vehicle. Radarinput is converted using "Signal Conversion Block
• To avoid the algebraic error "Unit delay block is used for the Acceleration Mode feedback.
ACC 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 the 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, the output signal (Signal Name LeadVehicle Speed) from requirement-1 is given as an input signal to this state machine
• Output from this subsystem a signal (Signal Name Acceleration Model which governs the vehicular speed of the drive vehicle which automatically adjust to speed & velocity to match the lead vehicle.
• The rest of the signal developed as per the Requirement
2. DEVELOPMENT OF MATLAB MODEL
This is the main model subsystem that having 9 inputs CameraInput_LeadVehicle, RadarInput_LeadVehicle, CameraInput_DriveVehicle, RadarInput_DriveVehi, Time_Gap, Set_Speed, Set_Gap, CruiseSwitch and SetSwitch entering into the Adaptive Cruise Control subsystem and 4 outputs Acceleration_Mode, LeadVehicle_Speed, DriveVehicle_Speed, LeadVehicle_Detected
There are 3 requirements are designed in 2 subsystems and one state machine flow chart
Requirement 1– Lead Vehicle:
Requirement 2 – Drive Vehicle:
Requirement 3 – Adaptive Cruise Control Algorithm:
Requirement 3a – ACC OFF MODE state logic:
Requirement 3b – ACC STANDBY MODE state logic:
Requirement 3c – ACC ON MODE state logic:
This state will be activated when the input signal SetSwitch is equal to 1. There are 6 sub-states to this state logic: They are:
Requirement 3c (i) – LeadVehicle_Detected_Follow (ACC ON MODE):
Requirement 3c (ii) – LeadVehicle_Not_Detected (ACC ON MODE):
Requirement 3c (iii) – LeadVehicle_Detected_Resume (ACC ON MODE):
Requirement 3c (iv) - LeadVehicle_Not_Detected_Resume (ACC ON MODE):
Requirement 3c (v) - LeadVehicle_Speed_lessthan_Set_Speed (ACC ON MODE):
Requirement 3c (vi) - LeadVehicle_Speed_equal_Set_Speed (ACC ON MODE):
3. Requirement Tagging and Tracebility report
all the requirements are tagged for better understandability, anyone who is reviewing the model or who is trying to update the model can get a clear idea of what logic is used in the system. from the requirement manager, we can see what are all are tagged and the description.
4. Developing SLDD
all the inputs, outputs, calibration parameters are saved in SLDD so while simulation the model takes all the values and its parameters from the Simulink Data Dictionary
5. Model Update.
The model update is done by Ctrl-D. this is done to check for any static errors. If no errors are coming the model is working fine without any errors.
6. Simulation
The model is simulated too for checking for errors and warnings
7. Configuration Parameter Update
Various configuration parameters are updated as per the code generation the solver type should be fixed for code generation
8. Model Advisory Report
By running the model advisor tool, we can see commonly made modelling errors.
9. Code generation
Build a model for generating code
To generate the code generation shortcut key is ctrl+B. It generates the code.
Generated Code
File: ADAS_Project_2.c
1 /*
2 * File: ADAS_Project_2.c
3 *
4 * Code generated for Simulink model 'ADAS_Project_2'.
5 *
6 * Model version : 1.8
7 * Simulink Coder version : 9.1 (R2019a) 23-Nov-2018
8 * C/C++ source code generated on : Sun Oct 2 22:02:56 2022
9 *
10 * Target selection: ert.tlc
11 * Embedded hardware selection: Intel->x86-64 (Windows64)
12 * Code generation objectives: Unspecified
13 * Validation result: Not run
14 */
15
16 #include "ADAS_Project_2.h"
17 #include "ADAS_Project_2_private.h"
18
19 /* Named constants for Chart: '<S1>/Chart' */
20 #define ADAS_Project_2_IN_OFF_MODE ((uint8_T)1U)
21 #define ADAS_Project_2_IN_ON_MODE ((uint8_T)2U)
22 #define ADAS_Project_2_IN_STANDBY_MODE ((uint8_T)3U)
23 #define ADAS_Project_IN_NO_ACTIVE_CHILD ((uint8_T)0U)
24 #define ADA_IN_LeadVehicle_Not_Detected ((uint8_T)3U)
25 #define IN_LeadVehicle_Detected_Follow ((uint8_T)1U)
26 #define IN_LeadVehicle_Detected_Resume ((uint8_T)2U)
27 #define IN_LeadVehicle_Not_Detected_Res ((uint8_T)4U)
28 #define IN_LeadVehicle_Speed_equal_Set_ ((uint8_T)5U)
29 #define IN_LeadVehicle_Speed_lessthan_S ((uint8_T)6U)
30
31 /* Exported data definition */
32
33 /* Definition for custom storage class: ExportToFile */
34 uint8_T Acceleration_Mode;
35 uint8_T DriveVehicle_Speed;
36 uint8_T LeadVehicle_Detected;
37 uint8_T LeadVehicle_Speed;
38
39 /* Block states (default storage) */
40 DW_ADAS_Project_2_T ADAS_Project_2_DW;
41
42 /* Real-time model */
43 RT_MODEL_ADAS_Project_2_T ADAS_Project_2_M_;
44 RT_MODEL_ADAS_Project_2_T *const ADAS_Project_2_M = &ADAS_Project_2_M_;
45
46 /* Model step function */
47 void ADAS_Project_2_step(void)
48 {
49 /* Sum: '<S4>/Add' incorporates:
50 * Inport: '<Root>/CameraInput_DriveVehicle'
51 * Inport: '<Root>/RadarInput_DriveVehicle'
52 * UnitDelay: '<S1>/Unit Delay'
53 */
54 DriveVehicle_Speed = (uint8_T)((uint32_T)(uint8_T)((uint32_T)
55 CameraInput_DriveVehicle + Acceleration_Mode) + RadarInput_DriveVehicle);
56
57 /* SignalConversion: '<S4>/Signal Conversion' incorporates:
58 * Inport: '<Root>/RadarInput_DriveVehicle'
59 */
60 LeadVehicle_Detected = RadarInput_DriveVehicle;
61
62 /* Sum: '<S3>/Add' incorporates:
63 * Inport: '<Root>/CameraInput_LeadVehicle'
64 * Inport: '<Root>/RadarInput_LeadVehicle'
65 */
66 LeadVehicle_Speed = (uint8_T)((uint32_T)CameraInput_LeadVehicle +
67 RadarInput_LeadVehicle);
68
69 /* Chart: '<S1>/Chart' incorporates:
70 * Inport: '<Root>/CruiseSwitch'
71 * Inport: '<Root>/SetSwitch'
72 * Inport: '<Root>/Set_Gap'
73 * Inport: '<Root>/Set_Speed'
74 * Inport: '<Root>/Time_Gap'
75 */
76 if (ADAS_Project_2_DW.is_active_c3_ADAS_Project_2 == 0U) {
77 ADAS_Project_2_DW.is_active_c3_ADAS_Project_2 = 1U;
78 ADAS_Project_2_DW.is_c3_ADAS_Project_2 = ADAS_Project_2_IN_OFF_MODE;
79 Acceleration_Mode = 0;
80 } else {
81 switch (ADAS_Project_2_DW.is_c3_ADAS_Project_2) {
82 case ADAS_Project_2_IN_OFF_MODE:
83 if (CruiseSwitch) {
84 ADAS_Project_2_DW.is_c3_ADAS_Project_2 = ADAS_Project_2_IN_STANDBY_MODE;
85 Acceleration_Mode = 1;
86 }
87 break;
88
89 case ADAS_Project_2_IN_ON_MODE:
90 if (!SetSwitch) {
91 ADAS_Project_2_DW.is_ON_MODE = ADAS_Project_IN_NO_ACTIVE_CHILD;
92 ADAS_Project_2_DW.is_c3_ADAS_Project_2 = ADAS_Project_2_IN_STANDBY_MODE;
93 Acceleration_Mode = 1;
94 } else if (!CruiseSwitch) {
95 ADAS_Project_2_DW.is_ON_MODE = ADAS_Project_IN_NO_ACTIVE_CHILD;
96 ADAS_Project_2_DW.is_c3_ADAS_Project_2 = ADAS_Project_2_IN_OFF_MODE;
97 Acceleration_Mode = 0;
98 } else {
99 switch (ADAS_Project_2_DW.is_ON_MODE) {
100 case IN_LeadVehicle_Detected_Follow:
101 if (LeadVehicle_Detected == 0) {
102 ADAS_Project_2_DW.is_ON_MODE = ADA_IN_LeadVehicle_Not_Detected;
103 } else {
104 if (((LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed))
105 || (Time_Gap < Set_Gap)) {
106 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Speed_lessthan_S;
107 }
108 }
109 break;
110
111 case IN_LeadVehicle_Detected_Resume:
112 if ((DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >=
113 Set_Speed) && (Time_Gap >= Set_Gap)) {
114 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Detected_Follow;
115 } else if ((DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed >
116 DriveVehicle_Speed) && (Time_Gap >= Set_Gap)) {
117 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Speed_equal_Set_;
118 } else {
119 if (LeadVehicle_Detected == 0) {
120 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Not_Detected_Res;
121 }
122 }
123 break;
124
125 case ADA_IN_LeadVehicle_Not_Detected:
126 if ((LeadVehicle_Detected == 1) && (DriveVehicle_Speed == Set_Speed) &&
127 (LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)) {
128 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Detected_Follow;
129 } else {
130 if (((LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed))
131 || (Time_Gap < Set_Gap)) {
132 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Speed_lessthan_S;
133 }
134 }
135 break;
136
137 case IN_LeadVehicle_Not_Detected_Res:
138 break;
139
140 case IN_LeadVehicle_Speed_equal_Set_:
141 if ((LeadVehicle_Detected == 0) || (DriveVehicle_Speed <= Set_Speed))
142 {
143 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Not_Detected_Res;
144 } else if (((DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed >
145 DriveVehicle_Speed)) || (Time_Gap >= Set_Gap)) {
146 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Detected_Resume;
147 } else {
148 if (((LeadVehicle_Speed < Set_Speed) && (LeadVehicle_Speed <
149 DriveVehicle_Speed)) || (0.75 * (real_T)Set_Gap == Time_Gap))
150 {
151 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Speed_lessthan_S;
152 }
153 }
154 break;
155
156 default:
157 /* case IN_LeadVehicle_Speed_lessthan_Set_Speed: */
158 if ((LeadVehicle_Detected == 0) && (DriveVehicle_Speed == Set_Speed))
159 {
160 ADAS_Project_2_DW.is_ON_MODE = ADA_IN_LeadVehicle_Not_Detected;
161 } else {
162 if (((real_T)LeadVehicle_Speed * 1.25 >= DriveVehicle_Speed) &&
163 ((real_T)LeadVehicle_Speed * 0.75 <= DriveVehicle_Speed) &&
164 (DriveVehicle_Speed < Set_Speed) && ((Time_Gap <= 1.25 * (real_T)
165 Set_Gap) && (Time_Gap >= 0.75 * (real_T)Set_Gap))) {
166 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Speed_equal_Set_;
167 }
168 }
169 break;
170 }
171 }
172 break;
173
174 default:
175 /* case IN_STANDBY_MODE: */
176 if (SetSwitch) {
177 ADAS_Project_2_DW.is_c3_ADAS_Project_2 = ADAS_Project_2_IN_ON_MODE;
178 ADAS_Project_2_DW.is_ON_MODE = IN_LeadVehicle_Detected_Follow;
179 } else {
180 if (!CruiseSwitch) {
181 ADAS_Project_2_DW.is_c3_ADAS_Project_2 = ADAS_Project_2_IN_OFF_MODE;
182 Acceleration_Mode = 0;
183 }
184 }
185 break;
186 }
187 }
188
189 /* End of Chart: '<S1>/Chart' */
190 }
191
192 /* Model initialize function */
193 void ADAS_Project_2_initialize(void)
194 {
195 /* Registration code */
196
197 /* initialize error status */
198 rtmSetErrorStatus(ADAS_Project_2_M, (NULL));
199
200 /* block I/O */
201
202 /* custom signals */
203 DriveVehicle_Speed = 0U;
204 LeadVehicle_Detected = 0U;
205 LeadVehicle_Speed = 0U;
206 Acceleration_Mode = 0U;
207
208 /* states (dwork) */
209 (void) memset((void *)&ADAS_Project_2_DW, 0,
210 sizeof(DW_ADAS_Project_2_T));
211
212 /* SystemInitialize for Chart: '<S1>/Chart' */
213 ADAS_Project_2_DW.is_ON_MODE = ADAS_Project_IN_NO_ACTIVE_CHILD;
214 ADAS_Project_2_DW.is_active_c3_ADAS_Project_2 = 0U;
215 ADAS_Project_2_DW.is_c3_ADAS_Project_2 = ADAS_Project_IN_NO_ACTIVE_CHILD;
216 Acceleration_Mode = 0;
217 }
218
219 /* Model terminate function */
220 void ADAS_Project_2_terminate(void)
221 {
222 /* (no terminate code required) */
223 }
224
225 /*
226 * File trailer for generated code.
227 *
228 * [EOF]
229 */
230
Results:
• After implementing the ACC model run the simulation. So the model is running successfully without any errors then the Adaptive Cruise Control works as per the requirement
• The model is running successfully without any error.
Conclusion:
Hence. The Adaptive Cruise Control feature is developed as per the Requirement Document using MATLAB Simulink
--------------------------------------*******************-----------------------------------------
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