Sequential Spatial Network for Collision Avoidance in Autonomous Driving. (arXiv:2303.07352v1 [cs.CV])

Several autonomous driving strategies have been applied to autonomous
vehicles, especially in the collision avoidance area. The purpose of collision
avoidance is achieved by adjusting the trajectory of autonomous vehicles (AV)
to avoid intersection or overlap with the trajectory of surrounding vehicles. A
large number of sophisticated vision algorithms have been designed for target
inspection, classification, and other tasks, such as ResNet, YOLO, etc., which
have achieved excellent performance in vision tasks because of their ability to
accurately and quickly capture regional features. However, due to the
variability of different tasks, the above models achieve good performance in
capturing small regions but are still insufficient in correlating the regional
features of the input image with each other. In this paper, we aim to solve
this problem and develop an algorithm that takes into account the advantages of
CNN in capturing regional features while establishing feature correlation
between regions using variants of attention. Finally, our model achieves better
performance in the test set of L5Kit compared to the other vision models. The
average number of collisions is 19.4 per 10000 frames of driving distance,
which greatly improves the success rate of collision avoidance.