Dynamic obstacle avoidance model of autonomous driving with attention mechanism and temporal residual block

Abstract

Dynamic obstacle avoidance is crucial in autonomous driving, ensuring vehicle safety by preventing collisions and enhancing driving efficiency. Dynamic obstacle avoidance algorithms have made significant progress due to deep learning. However, video-based target detection methods can suffer from missed or false detections when processing consecutive frames, especially for high-speed moving targets or in complex dynamic scenes. Multi-target tracking methods require intricate algorithm designs for target initialization and occluded object recovery, which can be compromised by tracker performance, leading to unstable tracking or target loss. To address the issue of target loss in multi-target tracking, we designed the novel YTCN model that infuses time series information through temporal convolution and enhances the sensitivity of the receptive field with Spatial Pyramid Pooling, Feature Concatenate and Spatial Convolution(SPPFCSPC), enhancing the model’s feature extraction capability. Simultaneously, we developed the novel Global Attention Mechanism(GAM) and Double Attention(DA) mechanisms that merge channel and spatial features to enhance feature representation. Finally we design the Temporal Residual Block(TRB) to model the temporal obstacles. Experimental results show that our method achieves 82.4 % of mAP_0.5 on the BDD 100 K dataset, which gets 1.3 % higher than the previous methods.