Current issues in improving technical vision for use in vehicles

Aleksandr G. Tynyanyy; Sergey R. Kristalniy; Pavel A., Krasavin; Maksim A. Toporkov; Aleksey N. Andreev

Current issues in improving technical vision for use in vehicles

Aleksandr G. Tynyanyy, Sergey R. Kristalniy, Pavel A., Krasavin, Maksim A. Toporkov, Aleksey N. Andreev

1. Evolyutsiya arkhitektur neyrosetey v komp'yuternom zrenii: segmentatsiya izobrazheniy, available at: https://habr.com/ru/companies/slsoft/articles/864994/?ysclid =mi7e2qugg9143782482 (24.10.2025). 2. Semantic Segmentation Using Deep Learning, MATLAB & Simulink, available at: https://www.mathworks.com/help/vision/ug/semantic-segmentation-using-deep-learning.html#d119e211 (20.12.2024). 3. Karpathy A. PyTorch at Tesla, available at: https://www.youtube.com/watch?v=oBklltKXtDE&ab_channel=PyTorch (15.07.2025). 4. Goodfellow I. Bengio Y., Courville A. Deep Learning, Goodfellow, Massachusetts, MIT Press, 2016, 800 p., ISBN 0262035618. 5. Kingma Diederik P., Ba J. Adam: A Method for Stochastic Optimization, available at: https://arxiv.org/abs/1412.6980 (15.07.2025). 6. Ren S., HeK., Girshick R., Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE transactions on pattern analysis and machine intelligence, 2017, vol. 39, no. 6, pp. 1137-1149, doi 10.1109/TPAMI.2016.2577031. 7. Wang J., Chen K., Yang Sh., Loy Ch.Ch. Region Proposal by Guided Anchoring, IEEE/CVF conference on computer vision and pattern recognition, Proceedings of the conference, 2019, pp. 2965-2974, doi 10.1109/cvpr.2019.00308. 8. Girshick R., Donahue J., Darrell T., Malik J. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation, IEEE transactions on pattern analysis and machine intelligence, 2016, vol. 38, no. 1, pp. 142-158, doi 10.1109/TPAMI.2015.2437384. 9. Semanticheskaya segmentatsiya s vysoty ptich'yego poleta dlya avtonomnogo vozhdeniya, available at: https://readmedium.com/monocular-birds-eye-view-semantic-segmentation-for-autonomous-driving-ee2f771afb59 (05.10.20250). 10. Obratnoye preobrazovaniye perspektivy s pomoshch'yu ruchnoy tochki skhoda, available at: https://github.com/osvaldlaszlo/inverse-perspective-mapping?tab=readme-ov-file (05.10.2025). 11. Haseeb MA., Guan J., Ristic-Durrant D., Gräser A. DisNet: a novel method for distance estimation from monocular camera, IEEE/RSJ international conference on intelligent robots and systems – IROS 2018, 10th workshop on planning, perception and navigation for intelligent vehicles (PPNIV), 2018. 12. Nair R.S., Supriya P. Robotic Path Planning Using Recurrent Neural Networks, 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2020,. pp. 1-5, doi 10/1109/ICCCNT49239.2020.9225479. 13. Xiao Yi., Codevilla F., Gurram A., Urfalioglu O., Lopez A.M. Multimodal End-to-End Autonomous Driving, IEEE Transactions on Intelligent Transportation Systems, 2022, vol. 23, no. 1, pp. 537-547, doi 10.1109/tits.2020.3013234. 14. Rusu P., Petriu E.M., Whalen T.E., Cornell A., Spoelder H.J.W. Behavior-based neuro-fuzzy controller for mobile robot navigation, IEEE Transactions on Instrumentation and Measurement, 2003, vol. 52, no. 4, pp. 1335-1340, doi 10.1109/TIM.003.816846. 15. Dao M., Lanza D., Fremont V. End-to-End Deep Neural Network Design for Short-term Path Planning, available at: https://hal.archives-ouvertes.fr/hal-02266802 (15.07.2025).

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Abstract

Technical vision systems are actively used in automotive transport. The development of this area leads to an increase in the number of onboard cameras installed on vehicles. Using various image analysis algorithms and neural networks, it is possible to obtain information about the vehicle's surroundings and the properties of nearby objects. An important feature is the positioning of objects in space and the determination of their distance. These functions are used to determine a safe distance and to create an optimal vehicle trajectory in a given space. This article discusses various methods of image analysis for use in car assistance systems or even in self-driving cars.

Keywords

technical vision, environmental detection, image-based distance measurement, neural networks, and driver assistance systems

About the Authors

Aleksandr G. Tynyanyy

MADI
Russian Federation

postgraduate student of the Department of Automobiles

Sergey R. Kristalniy

MADI
Russian Federation

Candidate of Sciences (Technical), associate professor of the Department of Automobiles

Pavel A., Krasavin

MADI
Russian Federation

Candidate of Sciences (Technical), associate professor of the Department of Automobiles,

Maksim A. Toporkov

MADI
Russian Federation

Candidate of Sciences (Technical), associate professor of the Department of Automobiles

Aleksey N. Andreev

MADI
Russian Federation

senior lecturer of the Department of Automobiles

References

1. Evolution of Neural Network Architectures in Computer Vision: Image Segmentation. Available at: https://habr.com/ru/companies/slsoft/articles/864994/?ysclid=mi7e2qugg9143782482.

2. Semantic Segmentation Using Deep Learning - MATLAB & Simulink. Available at: https://www.mathworks.com/help/vision/ug/semantic-segmentation-using-deep-learning.html#d119e211.

3. A. Karpathy. PyTorch at Tesla. Scientific and technical report and presentation, PyTorch DEVCON-19, 06.11.2019. Available at: https://www.youtube.com/watch?v=oBklltKXtDE&ab_channel=PyTorch.

4. Goodfellow I., Bengio Y., Courville A. Deep Learning. Massachusetts: MIT Press. 2016.

5. Rumelhart, D., Hinton, G. & Williams, R. Learning representations by back-propagating errors // Nature. 1986. pp. 533-536

6. Diederik P. Kingma, Ba J. Adam: A Method for Stochastic Optimization. URL: https://arxiv.org/abs/1412.6980.

7. Ren S. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks // IEEE Trans. Pattern Anal. Mach. Intell. 2017. Т. 39. № 6. С. 1137–1149.

8. Wang J. Region Proposal by Guided Anchoring // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019.

9. Girshick R. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation // IEEE Trans. Pattern Anal. Mach. Intell. 2016. Т. 38. № 1. С. 142–158.

10. Monocular Bird’s-Eye-View Semantic Segmentation for Autonomous Driving. Available at: https://readmedium.com/monocular-birds-eye-view-semantic-segmentation-for-autonomous-driving-ee2f771afb59.

11. Reverse perspective transformation using a manual vanishing point. Available at: https://github.com/osvaldlaszlo/inverse-perspective-mapping?tab=readme-ov-file.

12. Haseeb MA, Guan J, Ristić-Durrant D, et al. DisNet: a novel method for distance estimation from monocular camera. // IEEE/RSJ international conference on intelligent robots and systems – IROS 2018, 10th workshop on planning, perception and navigation for intelligent vehicles (PPNIV). 2018

13. Nair R. S., Supriya P. Robotic Path Planning Using Recurrent Neural Networks // 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2020.

14. Xiao Y. и др. Multimodal End-to-End Autonomous Driving // IEEE Trans. Intell. Transport. Syst. 2022. Т. 23. № 1. С. 537–547.

15. Rusu P. и др. Behavior-based neuro-fuzzy controller for mobile robot navigation // IEEE Trans. Instrum. Meas. 2003. Т. 52. № 4. С. 1335–1340.

16. Dao M., Lanza D., Fremont V., End-to-End Deep Neural Network Design for Short-term Path Planning. Available at: https://hal.archives-ouvertes.fr/hal-02266802.

Review

Рецензент: А.Е. Есаков, канд. техн. наук, доц., Московский политехнический университет

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Current issues in improving technical vision for use in vehicles

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Abstract

Keywords

About the Authors

References

Review

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