To address the limited detection targets, slow processing speed, and low accuracyof existing methods for driving obstacle prediction, this paper proposed an improved convolutional neural network called Coll-Net merged with spatial attention, a suitable speed control policy, and an obstacle direction determination method based on Coll-Net. Coll-Net imitated the vision mechanism of judging obstacles during driving, preprocessed the input monocular vision images to obtain the region of interest, and extracted the spatial features using a deep residual network framework. After collecting the spatial features, Coll-Net recalibrated the original features on the spatial feature channels by using the mechanism of spatial attention, which evaluated the features of every channel,improved the important ones, and then rescaled the weights of every channel and assigned the normalized weights to the corresponding spatial features in order to select critical features. The output feature map is connected by a fullyconnected layer; then,a normalized obstacle probability range of 0 to 1 is generated by a sigmoid function. Moreover, this paper proposes a driving policy, that controls the driving speed and predicts the obstacle direction according to the generated probability by Coll-Net. Experiment results indicate that Coll-Net prediction accuracy on standard datasets reaches 96.01% and the f1 score reaches 0.915. Coll-Net performs well in detecting diverse obstacles such as cars, pedestrians, guardrails, and walls in real time(24 ms for inference), as well as in low-contrast conditions. Moreover, the driving policy based on Coll-Net is validated using Udacity Self-Driving datasets.