Accesso libero

3D Occupancy Network Modelling for Multi-view Image Fusion Techniques in Autonomous Driving

Xingyu Hu

Xingyu Hu

Department of Vehicle Engineering, Shanghai University of Engineering ScienceShanghai, China
Cerca questo autore su
Sciendo|Google Scholar
Hu, Xingyu
, 
Xipei Ma

Xipei Ma

Department of Vehicle Engineering, Shanghai University of Engineering ScienceShanghai, China
Cerca questo autore su
Sciendo|Google Scholar
Ma, Xipei
, 
Pingqing Fan

Pingqing Fan

Department of Vehicle Engineering, Shanghai University of Engineering ScienceShanghai, China
Cerca questo autore su
Sciendo|Google Scholar
Fan, Pingqing
 e 
Chao Yang

Chao Yang

Department of Vehicle Engineering, Shanghai University of Engineering ScienceShanghai, China
Cerca questo autore su
Sciendo|Google Scholar
Yang, Chao
  
05 feb 2025
3D Occupancy Network Modelling for Multi-view Image Fusion Techniques in Autonomous Driving's Cover Image
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Cita
Scarica la copertina
Pubblicato online: 05 feb 2025
Ricevuto: 27 ago 2024
Accettato: 17 dic 2024
DOI: https://doi.org/10.2478/amns-2025-0060
Parole chiave
© 2025 Xingyu Hu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Optical principle of equivalent spherical model method
Optical principle of equivalent spherical model method

Figure 2.

Create the right of the overlapping region and the two search trees
Create the right of the overlapping region and the two search trees

Figure 3.

Schematic diagram of equivalent front wheel angle
Schematic diagram of equivalent front wheel angle

Figure 4.

Kinematics model of vehicle
Kinematics model of vehicle

Figure 5.

The structure of the fusion mechanism
The structure of the fusion mechanism

Figure 6.

Training set loss function
Training set loss function

Figure 7.

Verification set loss function
Verification set loss function

Figure 8.

Comparison of vehicle detection accuracy
Comparison of vehicle detection accuracy

Figure 9.

The following process of the path after the 3 species
The following process of the path after the 3 species

Figure 10.

Map 4 results from the frame simulation
Map 4 results from the frame simulation

The average result of 30 simulated results is treated after processing

Map Path Path length/m Length reduction/% Path number Smoothing ascension
1 Primary path 1174.42 - 8.6 -
Reverse optimization path 1172.43 0.17 3.5 59.30%
Spline smooth path 1171.38 0.26 0.5 94.19%
2 Primary path 1179.26 - 9.7 -
Reverse optimization path 1174.32 0.42 3.5 63.92%
Spline smooth path 1172.21 0.60 0.5 94.85%
3 Primary path 1141.08 11.6 -
Reverse optimization path 1105.21 3.14 4.5 61.21%
Spline smooth path 1094.61 4.07 0.5 95.69%

The car distance test analysis results

Actual distance/m Prediction distance/m Absolute error/m Prediction error/m
5 5.11 0.11 2.15%
10 10.31 0.31 3.01%
15 15.25 0.25 1.64%
20 20.43 0.43 2.10%
25 25.91 0.91 3.51%
30 31.16 1.16 3.72%
35 35.85 0.85 2.37%
40 41.09 1.09 2.65%
45 43.20 -1.80 -4.17%
50 52.20 2.20 4.21%
55 57.76 2.76 4.78%
60 62.36 2.36 3.78%
65 67.76 2.76 4.07%
70 73.30 3.30 4.50%
75 79.65 4.65 5.84%
80 84.71 4.71 5.56%
85 89.89 4.89 5.44%
90 96.13 6.13 6.38%
95 101.38 6.38 6.29%
100 107.06 7.06 6.59%

Vehicle structure parameters

Vehicle parameter Symbol Unit Numerical value
Car width w m 0.99
Wheelbase l m 1.00
Front suspension lf m 0.38
After suspension lr m 0.49
Minimum turning radius Rg m 2.45
The maximum front wheel is turning to the angular velocity δmax m 0.5236
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
Feed RSS della rivista