Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles
Abstract
:1. Introduction
- Providing simulation-based solution to the problem highlighted by authors in [12] of multiple factor analysis.
- Use of the Mamdani Fuzzy Inference system to compute the values of factors involved in collisions.
- Highlighting the combination of leading factors involved in rear-end collision.
- The combination of these factors has never been used before for collision avoidance using a Fuzzy system.
- The proposed model can assist drivers during different conditions by switching the control to the vehicle. The simulation will show the switching of control in the simulation section.
2. Related Work
3. Proposed Methodology
3.1. Five Factors Description
3.1.1. Environmental Factors
3.1.2. Physical Factors
3.1.3. Driver Factor
3.1.4. Time Factor
3.1.5. Weekdays Factor
4. Proposed Algorithm
Algorithm 1: Control Structure for transferring vehicle control to and from vehilce |
Do{ |
CD->GetSensorValues(); |
Data = Fuzzification (CD);//calculate the current situation of each category |
Result = MFBRECAS (Data)//combined/integrated result of every category and factors |
If (Result==chances of accidents) |
Control_brak() //Take control of the driver. |
If (EF is high) |
Control_speed_generate_alert(); |
PY_F=result of physical factor obtained from Data variable |
If (PY_F is high) |
Control_Speed() |
DF=result of Day factor obtained from Data variable |
If (MF is high) |
Control_speed_send_message(); |
Control_Speed();//control_speed() will be called within this fucntion |
DF=result of driver factors obtained from Data variable |
If (DF is high) |
Control_brak() |
T_O_A=result of time factor obtained from Data variable |
If (T_O_A is high) |
{ |
Apply Brakes () |
Control Speed () |
} |
Else |
Control_back_to_driver() |
End if |
} |
While (1); |
5. Experiments
5.1. Fuzzy-Logic Based Experiments
5.2. Simulation-Based Experiments
6. Results
7. Discussion
Limitations
8. Conclusions
8.1. Future Work
8.2. Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Very Low | Low | Average | High | Very High |
---|---|---|---|---|
0 to 0.1 | 0.11 to 0.25 | 0.251 to 0.5 | 0.51 to 0.75 | 0.751 to 1 |
Variables Used | Meanings of Variables | Description of Variables | Function Name | Function Description |
---|---|---|---|---|
CD | Category Data | It will take input from sensors about every accident-causing factor | Get SensorValues() | Vehicle has sensors from which it can take information about different factors involved. |
Data | Sensor data | Calculate the current situation of each category | Fuzzification(); | Mamdaini membership function will take sensor values as input and apply fuzzy logic. |
Result | Chances of Accident (COA) | It contains the value of the chance of accident. If the value is high, then the required function will be called. | Controlbrak() | If fuzzy logic functions show higher chance of accident, then our proposed system will apply brakes. |
EF | Environmental Factor | Result of environmental factor obtained from data variable | ControlSpeed() & generate alert | It will accelerate or decelerate the vehicle’s speed (reduce/fast). |
TOA | Time Of Accident | Result of time factor obtained from Data variable | ControlSpeed() & generate alert | It will accelerate or decelerate the vehicle’s speed (reduce/fast). |
PYF | Physical Factor | Result of physical factor obtained from Data variable | Control speed send message() | It will accelerate or decelerate the vehicle’s speed (reduce/fast). |
WD | Weekday Factor | Result of weekday factor obtained from Data variable List | ControlSpeed() & generate alert | It will accelerate or decelerate the vehicle’s speed (reduce/fast). |
DF | Driver Factor | Result of driver factors obtained from Data variable | ControlSpeed() & generate alert | It will accelerate or decelerate the vehicle’s speed (reduce/fast). |
Sr. | DT | WD | PF | DE | DA | EF | COA |
---|---|---|---|---|---|---|---|
1 | 0.1(VL) | 0.25(L) | 0.12(L) | 0.25(L) | 0.15(L) | 0.15(L) | 0.12(L) |
2 | 0.3(L) | 0.15(L) | 0.17(L) | 0.35(AVG) | 0.2(L) | 0.3(AVG) | 0.20(L) |
3 | 0.3(L) | 0.15(L) | 0.15(L) | 0.12(L) | 0.3(AVG) | 0.2(L) | 0.20(L) |
4 | 0.2(L) | 0.15(L) | 0.2(L) | 0.35(AVG) | 0.25(AVG) | 0.40(AVG) | 0.26(AVG) |
5 | 0.1(VL) | 0.03(VL) | 0.07(VL) | 0.35(L) | 0.3(AVG) | 0.23(L) | 0.08(VL) |
6 | 0.5(AVG) | 0.39(AVG) | 0.2(L) | 0.45(AVG) | 0.45(AVG) | 0.28(AVG) | 0.3(AVG) |
7 | 0.45(AVG) | 0.03(VL) | 0.2(L) | 0.42(AVG) | 0.35(AVG) | 0.44(AVG) | 0.28(AVG) |
8 | 0.5(AVG) | 0.39(AVG) | 0.35(AVG) | 0.5(AVG) | 0.5(AVG) | 0.46(AVG) | 0.44(AVG) |
9 | 0.6(H) | 0.53(H) | 0.4(AVG) | 0.6(H) | 0.5(AVG) | 0.5(AVG) | 0.51(H) |
10 | 0.6(H) | 0.32(L) | 0.6(H) | 0.35(AVG) | 0.7(H) | 0.65(H) | 0.6(H) |
11 | 0.45(AVG) | 0.32(L) | 0.4(AVG) | 0.55(H) | 0.7(H) | 0.69(H) | 0.75(H) |
12 | 0.7(H) | 0.5(AVG) | 0.2(L) | 0.45(AVG) | 0.9(VH) | 0.85(H) | 0.82(VH) |
13 | 0.7(H) | 0.5(AVG) | 0.6(H) | 0.55(H) | 0.9(VH) | 0.85(VH) | 0.9(VH) |
14 | 0.8(H) | 0.5(AVG) | 0.4(AVG) | 0.65(H) | 0.95(VH) | 0.9(H) | 0.97(VH) |
Exp # | EF | PF | DF | TF | WF | Collisions without Factors | Collisions with Factors |
---|---|---|---|---|---|---|---|
1 | 0.3 | 0.4 | 0.8 | 0.45 | 0.91 | 65 | 4 |
2 | 0.46 | 0.8 | 0.7 | 0.55 | 0.48 | 17 | 0 |
3 | 0.3 | 0.4 | 0.4 | 0.51 | 0.45 | 26 | 2 |
4 | 0.7 | 0.8 | 0.7 | 0.85 | 0.48 | 46 | 3 |
5 | 0.7 | 0.8 | 0.7 | 0.25 | 0.44 | 47 | 7 |
6 | 0.7 | 0.8 | 0.8 | 0.15 | 0.54 | 314 | 23 |
7 | 0.3 | 0.4 | 0.8 | 0.95 | 0.90 | 404 | 15 |
8 | 0.7 | 0.6 | 0.8 | 0.55 | 0.45 | 28 | 0 |
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Razzaq, S.; Dar, A.R.; Shah, M.A.; Khattak, H.A.; Ahmed, E.; El-Sherbeeny, A.M.; Lee, S.M.; Alkhaledi, K.; Rauf, H.T. Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles. Appl. Sci. 2022, 12, 1049. https://doi.org/10.3390/app12031049
Razzaq S, Dar AR, Shah MA, Khattak HA, Ahmed E, El-Sherbeeny AM, Lee SM, Alkhaledi K, Rauf HT. Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles. Applied Sciences. 2022; 12(3):1049. https://doi.org/10.3390/app12031049
Chicago/Turabian StyleRazzaq, Sheeba, Amil Roohani Dar, Munam Ali Shah, Hasan Ali Khattak, Ejaz Ahmed, Ahmed M. El-Sherbeeny, Seongkwan Mark Lee, Khaled Alkhaledi, and Hafiz Tayyab Rauf. 2022. "Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles" Applied Sciences 12, no. 3: 1049. https://doi.org/10.3390/app12031049
APA StyleRazzaq, S., Dar, A. R., Shah, M. A., Khattak, H. A., Ahmed, E., El-Sherbeeny, A. M., Lee, S. M., Alkhaledi, K., & Rauf, H. T. (2022). Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles. Applied Sciences, 12(3), 1049. https://doi.org/10.3390/app12031049