###### Abstract  
In a connected vehicle environment based on vehicle-to-vehicle (V2V) technology, images from front and ego vehicles are fused to augment a driver's or autonomous system's visual field, which is helpful in avoiding road accidents by eliminating the blind point (the objects occluded by vehicles), especially tailgating in urban areas. Realizing multi-view image fusion is a tough problem without knowing the relative location of two sensors and the fusing object is occluded in some views. Therefore, we propose an image geometric projection model and a new fusion method between neighbor vehicles in a cooperative way. Based on a 3D inter-vehicle projection model, selected feature matching points are adopted to estimate the geometric transformation parameters. By adding deep information, our method also designs a new deep-affine transformation to realize fusing of inter-vehicle images. Experimental results on KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) datasets are shown to validate our algorithm. Compared with previous work, our method improves the IoU index by 2-3 times. This algorithm can effectively enhance the visual perception ability of intelligent vehicles, and it will help to promote the further development and improvement of computer vision technology in the field of cooperative perception. oeoperative perception; visual augmentation; image fusion; vehicle-to-vehicle (V2V) technology; vehicle safety +
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alerts drivers. In [6], as an alternative to equipping the vehicle with expensive sensors, the binocular camera of smartphones or tablets can detect and track forward obstacles, vehicles, and lanes. A further study [7] proposes a time-based collision avoidance warning system (CSW) for lead vehicles in rear-end collisions. It directly quantifies the threat level of the current dynamic situation using velocity, acceleration, and the gap between vehicles. The authors of [8] propose a tailgating model used to monitor tailgating behavior of drivers. The tailgating model calculates the minimum gap required considering relative speed, driver's perception reaction time, weather conditions, and brake efficiency in real time, and alerts drivers with an audio or visual signal. All the rear-end collision avoidance systems mentioned above only used information obtained from sensors or cameras equipped in the host vehicles. Even the autonomous vehicle system also relies solely on ego-vehicle sensors. Their method has limitations in dealing with the collisions due to the presence of blind spots. If the blind spot can be translucent, the drivers could realize the situation before the sudden break of the leading vehicle occurred. Drivers can then have enough reaction time to avoid collision. Therefore, the risk can be decreased by utilizing sensed information from neighboring vehicles though vehicle-to-vehicle (V2V) communication [9]. Motivated by this deduction, we contribute to research in the field by elaborating on the cooperative system, formed from forward neighboring vehicles and the host vehicle, to augment the host vehicle driver's visual ability. Our method is valuable not only to the ADAS system but also to autonomous vehicle systems, which can improve driving safety by extending the visual perception to obstructed areas. Although many groups have presented their research on collaborative approaches for safe driving, finding an efficient way to enhance visual perception in order to guarantee safe (automated) driving is still an open question. In [10; 11], location information of vehicles is exchanged periodically to prevent potential danger. The authors of [4] provided a rear-end distance warning system based on images garnered from stereoscopic cameras on rear vehicles and rear cameras on leading vehicles. These cooperative systems gave text or digital information, such as a warning message, time gap between cars, and routing data.