A Vehicle Monocular Ranging Method Based on Camera Attitude Estimation and Distance Estimation Networks
Abstract
:1. Introduction
2. Camera Pose Estimation
2.1. Road Disappearance Point Detection
- In traffic scenarios, the slope of lane markings has certain characteristics. During driving, features such as road signs, greenery, and buildings that are close to vertical can be filtered. In addition, due to the fact that the characteristic lines of shadows and other shadows generated by lighting are mostly horizontal, they can be filtered. In practical operation, line segments smaller than 5° and larger than 85° are filtered to improve the accuracy of feature line segments.
- During normal driving, the road is not undulating and the lane markings are located in the lower half of the image. Therefore, the line segments in the upper half of the image can be filtered.
- The lane markings are clearly bright colors, with green indicating road greening in the bright colors. Therefore, the line segments with dark and green background colors can be filtered to improve the accuracy of feature line segments.
- Segment length
- Segment direction
2.2. Camera Pose
3. Monocular Ranging of the Forward Vehicle
3.1. Distance Measurement Based on Vehicle Grounding Point and Vehicle Width
3.2. The Influence of Attitude Angle on Distance Measurement
3.3. The Fusion of Two Ranging Schemes
4. Results
4.1. Ablation Experiment
4.2. Dataset Validation
4.3. Continuous Image Verification
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Block | Filter Size | Channel | Input |
---|---|---|---|
Encoder | |||
conv1 | 7 × 7 | 3/64 | Original image |
maxpool | 3 × 3 | 64/64 | F (conv1) |
layer1 | 3 × 3 | 64/256 | F (maxpool) |
Decoder | |||
reduction | 1 × 1 | 256/128 | F (layer1) |
dec3 | 3 × 3 | 128/1 | F (reduction) |
dec2up | 3 × 3 | 128/64 | F (dec3bneck) |
dec2reduc | 1 × 1 | 128/60 | F (conv1 ⊕ dec2up) |
dec2bneck | 3 × 3 | 64/64 | F (dec2reduc ⊕ R2 ⊕ dec3 *) |
dec2 | 3 × 3 | 64/1 | F (dec2bneck) |
dec1up | 3 × 3 | 64/60 | F (dec2bneck) |
dec1bneck | 3 × 3 | 64/64 | F (dec1up ⊕ R1 ⊕ dec2 *) |
dec1 | 3 × 3 | 64/1 | F (dec1bneck) |
Module | (a) | (b) | (c) | (d) |
---|---|---|---|---|
Ranging based on grounding point | √ | √ | √ | |
Ranging based on vehicle width | √ | √ | √ | |
Camera pose estimation | √ | √ | √ | |
Training network | √ | √ | ||
MSE ↓ | 1.23 | — | — | 1.51 |
Accuracy ↑ | 0.917 | 0.853 | 0.811 | 0.892 |
3Dbbox | Liu et al. [10] | Zhao et al. [11] | Proposed Method | |
---|---|---|---|---|
AbsRel ↓ | 0.222 | 0.095 | 0.085 | 0.083 |
SqRel ↓ | 1.863 | 0.454 | 0.375 | 0.402 |
RMS ↓ | 7.696 | 4.728 | 4.114 | 4.100 |
RMSlog ↓ | 0.228 | 0.153 | 0.108 | 0.121 |
δ < 1.251 ↑ | 0.659 | 0.903 | 0.974 | 0.952 |
δ < 1.252 ↑ | 0.966 | 0.995 | 0.997 | 0.998 |
δ < 1.253 ↑ | 0.994 | 1.000 | 1.000 | 1.000 |
No. | Pitch Angle | Yaw Angle | Distance | ||||||
---|---|---|---|---|---|---|---|---|---|
True Value | Estimated Value | Relative Error/% | True Value | Estimated Value | Relative Error/% | True Value/m | Estimated Value/m | Relative Error/% | |
1 | 1.31 | 1.86 | 41.98 | 2.78 | 3.02 | 8.63 | 27.3 | 26.94 | 1.32 |
2 | 3.27 | 3.49 | 6.73 | 5.08 | 5.24 | 3.15 | 37.3 | 36.7 | 1.61 |
3 | 4.96 | 5.08 | 2.42 | 7.81 | 8.21 | 5.12 | 47.3 | 46.98 | 2.79 |
4 | 7.53 | 7.97 | 5.84 | 9.36 | 9.69 | 3.53 | 57.3 | 55.77 | 2.67 |
5 | 11.87 | 12.35 | 4.04 | 14.85 | 15.42 | 3.84 | 67.3 | 65.04 | 3.36 |
6 | 14.38 | 14.89 | 3.55 | 16.97 | 17.51 | 3.18 | 77.3 | 74.51 | 3.61 |
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Liu, J.; Xu, D. A Vehicle Monocular Ranging Method Based on Camera Attitude Estimation and Distance Estimation Networks. World Electr. Veh. J. 2024, 15, 339. https://doi.org/10.3390/wevj15080339
Liu J, Xu D. A Vehicle Monocular Ranging Method Based on Camera Attitude Estimation and Distance Estimation Networks. World Electric Vehicle Journal. 2024; 15(8):339. https://doi.org/10.3390/wevj15080339
Chicago/Turabian StyleLiu, Jun, and Duo Xu. 2024. "A Vehicle Monocular Ranging Method Based on Camera Attitude Estimation and Distance Estimation Networks" World Electric Vehicle Journal 15, no. 8: 339. https://doi.org/10.3390/wevj15080339