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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Zeziulin, D. V. Tyugin, D. Y. Tumasov, A. V. Groshev, A. M. Porubov, D. M. |
| Copyright Year | 2018 |
| Abstract | In Russia, 36% [1] of all accidents are a collision with a pedestrian. Vehicle structural elements limit visibility to drivers and do not allow fully assessing the traffic situation, forming blind zones. It especially applies to commercial vehicles with large dimensions. The system allowing to expand visibility of the car due to use of displays in windscreen pillars is presented in this article. It is proposed to use the pedestrian recognition system integrated into the windscreen pillars based on neural networks. The system allows to detect and estimate distance to the objects and to give warning signals about possible collision. It consists of an on-board computer, video cameras and output devices. Interior elements of a commercial vehicle were developed and installed. External side mirrors were replaced with video cameras. The exterior elements were designed and installed. Further there were carried out experimental researches of the system. |
| Sponsorship | Design Engineering Division Computers and Information in Engineering Division |
| File Format | |
| ISBN | 9780791851784 |
| DOI | 10.1115/DETC2018-85382 |
| Volume Number | Volume 3: 20th International Conference on Advanced Vehicle Technologies; 15th International Conference on Design Education |
| Conference Proceedings | ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference |
| Language | English |
| Publisher Date | 2018-08-26 |
| Publisher Place | Quebec City, Quebec, Canada |
| Access Restriction | Subscribed |
| Subject Keyword | Computers Artificial neural networks Traffic Video cameras Dimensions Collision avoidance systems Vehicles Structural elements (construction) Collisions (physics) Signals Mirrors Columns (structural) Accidents |
| Content Type | Text |
| Resource Type | Article |
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