Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas
Author:
Pyo Jeong-WonORCID, Bae Sang-HyeonORCID, Joo Sung-HyeonORCID, Lee Mun-KyuORCID, Ghosh ArpanORCID, Kuc Tae-YongORCID
Abstract
Autonomous driving and its real-world implementation have been among the most actively studied topics in the past few years. In recent years, this growth has been accelerated by the development of advanced deep learning-based data processing technologies. Moreover, large automakers manufacture vehicles that can achieve partially or fully autonomous driving for driving on real roads. However, self-driving cars are limited to some areas with multi-lane roads, such as highways, and self-driving cars that drive in urban areas or residential complexes are still in the development stage. Among autonomous vehicles for various purposes, this paper focused on the development of autonomous vehicles for garbage collection in residential areas. Since we set the target environment of the vehicle as a residential complex, there is a difference from the target environment of a general autonomous vehicle. Therefore, in this paper, we defined ODD, including vehicle length, speed, and driving conditions for the development vehicle to drive in a residential area. In addition, to recognize the vehicle’s surroundings and respond to various situations, it is equipped with various sensors and additional devices that can notify the outside of the vehicle’s state or operate it in an emergency. In addition, an autonomous driving system capable of object recognition, lane recognition, route planning, vehicle manipulation, and abnormal situation detection was configured to suit the vehicle hardware and driving environment configured in this way. Finally, by performing autonomous driving in the actual experimental section with the developed vehicle, it was confirmed that the function of autonomous driving in the residential area works appropriately. Moreover, we confirmed that this vehicle would support garbage collection works through the experiment of work efficiency.
Funder
Technology Innovation Program
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference50 articles.
1. Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., and Van Gool, L. (2018, January 8–13). Towards End-to-End Lane Detection: An Instance Segmentation Approach. Proceedings of the IEEE Intelligent Vehicles Symposium, Rio de Janeiro, Brazil. 2. Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). ENet: A deep neural network architecture for real-time semantic segmentation. arXiv. 3. Yin, R., Yu, B., Wu, H., Song, Y., and Niu, R. (2020). FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation using Deep Neural Network, Lappeenranta University of Technology. arXiv. 4. Philion, J. (2019, January 16–21). FastDraw: Addressing the long tail of lane detection by adapting a sequential prediction network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Long Beach, CA, USA. 5. Zheng, T., Fang, H., Zhang, Y., Tang, W., Yang, Z., Liu, H., and Cai, D. (2020). Resa: Recurrent feature-shift aggregator for lane detection. arXiv.
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