Visual driving assistance system based on few-shot learning

Abstract

With the increase of vehicles and the diversification of road conditions, people pay more attention to the safety of driving. In recent years, autonomous driving technology by Franke et al. (IEEE Intell Syst Their Appl 13(6):40-48, 1998) and unmanned driving technology by Zhang et al. (CAAI Trans Intell Technol 1(1):4-13, 2016) have entered our field of vision. Both automatic driving by Levinson et al. (Towards fully autonomous driving: Systems and algorithms, 2011) and unmanned driving by Im et al. (Unmanned driving of intelligent robotic vehicle, 2009) use a variety of sensors to collect the environment around the vehicle, and use a variety of decision control algorithms to control the vehicle in motion. The visual driving assistance system by Watanabe, et al. (Driving assistancesystem for appropriately making the driver recognize another vehicle behind or next to present vehicle, 2010), used in conjunction with the target recognition algorithm by Pantofaru et al. (Object recognition by integrating multiple image segmentations, 2008)), will provide drivers with real-time environment around the vehicle. In recent years, few-shot learning by Li et al. (Comput Electron Agric 2:2, 2020) has become a new direction of target recognition algorithm, which reduces the difficulty of collecting training samples.In this paper, on one hand, several low-light cameras with fish-eye lenses are used to collect and reconstruct the environment around the vehicle. On the other hand, we use infrared camera and lidar to collect the environment in front of the vehicle. Then, we use the method of few-shot learning to identify vehicles and pedestrians in the forward-view image. In addition, we develop the system on embedded devices according to miniaturization requirements. In conclusion, the system will adapt to the needs of most drivers at this stage, and will effectively cooperate with the development of automatic driving and unmanned driving.

Practical Autonomous Driving: A Survey of Challenges and Opportunities

“…The few-shot learning algorithm is utilized to identify nearby vehicles and pedestrians in the front view. Additionally, the researchers have developed a comprehensive embedded visual assistance system that integrates all these devices into a miniature setup [104].…”

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

“…NLP determines the format of text and structure to identify the text's actual meaning and content, providing an accurate set of data for the data processing system in AVs. NLP uses a process called "knowledge discovery" that identifies the meaning of the text and improves the feasibility of AVs [19,20].…”

Fish-eye image effective region segmentation algorithm based on circle fitting

“…The segmentation of the valid region in fish-eye images is a key technology in panoramic image synthesis. In recent years, the synthesis of panoramic images from fish-eye images has been widely applied in areas such as driving assistance [7] , panoramic roaming [8] , and object detection [9] . To ensure synthesis quality, these synthesis methods use fisheye images that have been properly processed.…”