Résumé: 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 assistance system 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.

Langue: Anglais