A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles
<p>Vehicle axis system.</p> "> Figure 2
<p>The UWB based relative positioning system model.</p> "> Figure 3
<p>Two sets of solutions.</p> "> Figure 4
<p>Ackerman steering model.</p> "> Figure 5
<p>The relative kinematic model.</p> "> Figure 6
<p>Collision types. (<b>a</b>) Point-to-edge collision; (<b>b</b>) Edge-to-edge collision; (<b>c</b>) Point-to-point collision.</p> "> Figure 7
<p>The collision warning model.</p> "> Figure 8
<p>The virtual scenario in the driving scenario designer.</p> "> Figure 9
<p>Comparison of relative positioning and directing algorithm with and without the constraint of <span class="html-italic">d</span><sub>5</sub>: (<b>a</b>) The relative longitudinal position <span class="html-italic">x</span>; (<b>b</b>) The relative lateral position <span class="html-italic">y</span>; (<b>c</b>) The relative orientation <span class="html-italic">β</span>.</p> "> Figure 10
<p>Comparison of positioning and directing performance using UWB and fusion of UWB/DR: (<b>a</b>) The relative longitudinal position <span class="html-italic">x</span>; (<b>b</b>) The relative lateral position <span class="html-italic">y</span>; (<b>c</b>) The relative orientation <span class="html-italic">β</span>.</p> "> Figure 11
<p>Comparison of yaw rates measured by DR and estimated by UWB/DR: (<b>a</b>) Yaw rate of vehicle 1; (<b>b</b>) Yaw rate of vehicle 2.</p> "> Figure 12
<p>Comparison of velocities measured by DR and estimated by UWB/DR: (<b>a</b>) Velocity of vehicle 1; (<b>b</b>) Velocity of vehicle 2.</p> "> Figure 13
<p>TTC simulating scenarios.</p> "> Figure 14
<p>TTC estimation error.</p> "> Figure 15
<p>Experimental Equipment.</p> "> Figure 16
<p>The testing ground and vehicle driving routes.</p> "> Figure 17
<p>Vehicle state display software.</p> "> Figure 18
<p>Test 1 in the straight driving experiments.</p> "> Figure 19
<p>Test 2 in the straight driving experiments.</p> "> Figure 20
<p>Test 3 in the straight driving experiments.</p> "> Figure 21
<p>The transformation from Cartesian coordinates to polar coordinates.</p> "> Figure 22
<p>The results of the middle-distance experiments. (<b>a</b>) Relative distance; (<b>b</b>) Relative azimuth angle; (<b>c</b>) Relative velocity; (<b>d</b>) Relative orientation.</p> "> Figure 23
<p>The results of the long-distance experiments. (<b>a</b>) Relative distance; (<b>b</b>) Relative azimuth angle; (<b>c</b>) Relative velocity; (<b>d</b>) Relative orientation.</p> ">
Abstract
:1. Introduction
2. Algorithm and Modeling
2.1. The Relative Positioning and Directing System
2.2. The DR System Based on Wheel Speed Sensors
2.3. The EKF Based UWB/DR Fusion Model
2.4. The Collision Warning Model
3. Simulation
3.1. Simulation of the Overconstrained UWB Positioning and Directing System
3.2. Simulation of the UWB/DR Fusion Algorithm
3.3. Simulation of CWS based on TTC Estimation
- “Failed” denotes warning too late or not warning;
- “Correct” denotes warning in the proper time period;
- “False” denotes warning too early or warning by mistake.
4. Experiments
4.1. Experimental Equipment and Environment
4.2. Straight Driving Experiments
4.2.1. Test 1
4.2.2. Test 2
4.2.3. Test 3
4.2.4. Results Analysis of the Straight Driving Experiments
4.3. Curved Driving Experiments
4.3.1. Middle-Distance Experiments
4.3.2. Long-Distance Experiments
4.3.3. Results Analysis of the Curved Experiments
5. Conclusions
- The fusion method significantly improves the relative positioning/directing accuracy and slightly improves the velocity accuracy according to the simulation and experiment results.
- The proposed CWS passes the regulated tests in JT/T883-2014 published by MOT, which proves the feasibility of the proposed system.
- In middle-distance mode up to 50 m, compared to the MMWR, the proposed system improves the relative positioning/directing accuracy by 44%, 69%, and 8%, respectively, in the relative distance, azimuth angle, and velocity. As for in long-distance mode, the enhanced rate is 66% and 38%, respectively, for the relative distance and azimuth angle. The relative velocity accuracy of the proposed system is similar to the MMWR.
- In both middle and long-distance modes, the proposed system can provide relative orientations with errors no more than 0.4° RMSE, which is not available directly in MMWR systems, but it is very beneficial to the CWS.
Author Contributions
Funding
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Algorithm | RMSEx (m) | RMSEy (m) | RMSEβ (°) |
---|---|---|---|
With d5 | 0.70 | 0.73 | 46.29 |
Without d5 | 0.21 | 0.58 | 2.42 |
Algorithm | RMSEx (m) | RMSEy (m) | RMSEβ (°) |
---|---|---|---|
UWB | 0.21 | 0.58 | 2.42 |
UWB + DR (EKF) | 0.06 | 0.17 | 0.83 |
Algorithm | ||||
---|---|---|---|---|
DR | 8.92 | 8.71 | 0.14 | 0.15 |
UWB + DR (EKF) | 5.07 | 4.60 | 0.12 | 0.08 |
Parameters | Range |
---|---|
v1&v2 (km/h) | 0~75 |
x (m) | −200~200 |
y (m) | −15~15 |
β (°) | 0~360 |
Evaluation | Quantity |
---|---|
Failed | 0 |
Correct | 194 |
False | 2 |
Evaluation | Quantity |
---|---|
Failed | 0 |
Correct | 10,596 |
False | 227 |
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
---|---|---|---|---|---|---|---|
TTC(CWS) | 2.9987 | 2.9907 | 2.9759 | 2.9814 | 2.9729 | 2.9722 | 2.9799 |
TTC(Real) | 3.0047 | 3.0069 | 2.9925 | 2.9963 | 2.9902 | 3.0136 | 3.0219 |
Evaluation | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
---|---|---|---|---|---|---|---|
TTC(CWS) | 2.9863 | 2.9810 | 2.9987 | 2.9804 | 2.9954 | 2.9899 | 2.9673 |
TTC(Real) | 3.0423 | 3.1166 | 3.0245 | 3.0354 | 3.1283 | 3.1269 | 3.0226 |
Evaluation | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
1 | 2 | 3 | 4 | 5 | 6 | 7 | |
---|---|---|---|---|---|---|---|
TTC(CWS) | 2.9782 | 2.9905 | 2.9947 | 2.9789 | 2.9942 | 2.8623 | 2.8958 |
TTC(Real) | 2.7560 | 2.8110 | 2.7877 | 2.6851 | 2.6511 | 2.5831 | 2.8975 |
Evaluation | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
Mode | Coverage (m) | RMSEρ (m) | RMSEθ (°) | RMSEv (m/s) |
---|---|---|---|---|
Middle Distance | 50 | 0.25 | 1 | 0.12 |
Long Distance | 100 | 0.5 | 0.5 | 0.12 |
Mode | RMSEρ (m) | RMSEθ (°) | RMSEv (m/s) | RMSEβ (°) |
---|---|---|---|---|
No Fusion | 0.14 | 0.76 | 0.22 | 1.84 |
Fusion | 0.14 | 0.31 | 0.11 | 0.39 |
Mode | RMSEρ (m) | RMSEθ (°) | RMSEv (m/s) | RMSEβ (°) |
---|---|---|---|---|
No Fusion | 0.18 | 0.77 | 0.22 | 1.86 |
Fusion | 0.17 | 0.31 | 0.12 | 0.40 |
Mode | System | RMSEρ (m) | RMSEθ (°) | RMSEv (m/s) | RMSEβ (°) |
---|---|---|---|---|---|
Middle Distance | MMWR | 0.25 | 1 | 0.12 | None |
Proposed System (No Fusion) | 0.14 | 0.76 | 0.22 | 1.84 | |
Proposed System (Fusion) | 0.14 | 0.31 | 0.11 | 0.39 | |
Long Distance | MMWR | 0.5 | 0.5 | 0.12 | None |
Proposed System (No Fusion) | 0.18 | 0.77 | 0.24 | 1.86 | |
Proposed System (Fusion) | 0.17 | 0.31 | 0.12 | 0.40 |
Mode | RMSEρ (m) | RMSEθ (°) | RMSEv (m/s) |
---|---|---|---|
Middle Distance | 44% | 69% | 8% |
Long Distance | 66% | 38% | 0% |
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Wang, M.; Chen, X.; Jin, B.; Lv, P.; Wang, W.; Shen, Y. A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles. Sensors 2021, 21, 3485. https://doi.org/10.3390/s21103485
Wang M, Chen X, Jin B, Lv P, Wang W, Shen Y. A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles. Sensors. 2021; 21(10):3485. https://doi.org/10.3390/s21103485
Chicago/Turabian StyleWang, Mingyang, Xinbo Chen, Baobao Jin, Pengyuan Lv, Wei Wang, and Yong Shen. 2021. "A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles" Sensors 21, no. 10: 3485. https://doi.org/10.3390/s21103485