FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks

Yin, Ruochen; Yu, Biao; Wu, Huapeng; Song, Yutao; Niu, Runxin

doi:10.48550/arxiv.2003.04404

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Liu et al [ 10 ] improved the performance of lane detection using ERFNet [ 19 ] and images in low-light conditions generated by the proposed Better-CycleGAN. To overcome the limitations of the camera sensor, Yin et al [ 3 ] presented a lane detection method based on time by applying long short-term memory (LSTM) with the segmentation of the camera and LIDAR bird’s eye view using DeepLabv3+ [ 20 ]. Furthermore, Khanm et al [ 8 ] proposed an architecture integrated with VGG16 and gated recurrent unit (GRU) for lane-following on roads.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, with the rapid development of deep learning, the technology for recognizing the surrounding environment through sensor data has also developed significantly. These deep learning technologies are commonly used for recognizing lanes through cameras [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] or for recognizing surrounding objects [ 11 , 12 , 13 , 14 ], or as a method for simultaneously recognizing surrounding objects using the camera and lidar [ 15 , 16 ] in autonomous vehicles. Along with various high-precision sensors and deep learning technologies described above, research and commercialization of autonomous vehicles that can autonomously drive on roads without human intervention are also rapidly progressing.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Pyo

Bae

Joo

et al. 2022

Sensors

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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.

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