The lane line is difficult to be distinguished in visible light for driver assistance, so a multi-feature fusion model is built from the perspective of infrared images to realize assistant driving. Firstly, the features of infrared imaging are analyzed, and the Enet network is improved to focus on the area of the lane line and locate the passable area. Then, the previous vehicle is located to guide the current vehicle based on the fuzzy set theory. Finally, a spatiotemporal association model is constructed. By constructing the relationship between the guiding vehicle and spatiotemporal traffic, the relationship between human-computer interaction is indirectly established to guide vehicle assisted driving. Our experimental results show that the proposed algorithm is in line with the manual driving process, and good results can be achieved under complex road conditions.

车道线很难在可见光中区分出来，以帮助驾驶员，因此从红外图像的角度构建了一个多特征融合模型，以实现辅助驾驶。首先，分析了红外成像的特点，对Enet网络进行了改进，使其聚焦于车道线区域并确定了可通行区域。然后，基于模糊集理论将前一辆车定位为引导当前车。最后，建立了一个时空关联模型。通过构造引导车辆与时空交通之间的关系，间接建立人机交互之间的关系以引导车辆辅助驾驶。我们的实验结果表明，所提出的算法与手动驾驶过程相符，