Deep Learning in Perception of Autonomous Vehicles
- 10.2991/assehr.k.220110.107How to use a DOI?
- deep learning; autonomous vehicles; self-driving cars; perception; localization; object detection
With the development in deep learning and sensor technologies in recent years, the ultimate goal to build full autonomous vehicles (AV) has come closer to practicality. Autonomous vehicles need to be able to percept the environment in order to make the correct decision in controlling the vehicles under different situations. In addition, the process needs to be as accurate as possible since operation under safe conditions is one of the most important issues. Moreover, the efficiency of methods is another crucial factor since complicated traffic requires vehicles to be flexible and react accordingly. Besides, if an AV cannot operate properly and timely, it would be pointless to consider it as an alternative way to the human-control car. While the progress in sensors contributes to the development of AV, the data obtained by sensors is still possible to fail due to environmental or weather conditions. This article first gives a review of the concepts in AV, especially focusing on environmental perception, then concludes and compares several deep learning methods on environment perceptions in the field of AV.
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Yunxiang Jiang AU - Tengyu Hsiao PY - 2022 DA - 2022/01/28 TI - Deep Learning in Perception of Autonomous Vehicles BT - Proceedings of the 2021 International Conference on Public Art and Human Development ( ICPAHD 2021) PB - Atlantis Press SP - 561 EP - 565 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.220110.107 DO - 10.2991/assehr.k.220110.107 ID - Jiang2022 ER -