Predicting the future trajectories of multiple pedestrians in certain scenes is critical for autonomous moving platforms (like, self-driving cars and social robots). In this paper, we propose a novel Generative Adversarial Network model with Transformers, which simulates the pedestrian distribution to capture the uncertainty of the predicted paths and generate more reasonable future trajectories. The design of our method includes a generator and a discriminator. The generator mainly contains an encoder, a decoder, and a prediction module. Specifically, the encoder and the decoder comprise multihead convolutional selfattention to learn the sequence of historical movement, and the prediction module incorporates the Mish Feed-Forward Network to yield the predicted target. The discriminator takes both the predicted paths and ground truth as input, classifies them as socially acceptable or not. Experimental results show that the proposed method consistently boosts the performance of trajectory forecasting, and our framework surpasses several existing baselines by evaluating the results on various data sets. Code is available at https://github. com/lzz970818/Trajectory-Prediction.

Lane detection algorithms require extremely low computational costs as an important part of autonomous driving. Due to heavy backbone networks, algorithms based on pixel-wise segmentation is struggling to handle the problem of runtime consumption in the recognition of lanes. In this paper, a novel and practical methodology based on lightweight Segmentation Network is proposed, which aims to achieve accurate and efficient lane detection. Different with traditional convolutional layers, the proposed Shadow module can reduce the computational cost of the backbone network by performing linear transformations on intrinsic feature maps. Thus a lightweight backbone network Shadow-VGG-16 is built. After that, a tailored pyramid parsing module is introduced to collect different sub-domain features, which is composed of both a strip pool module based on Pyramid Scene Parsing Network (PSPNet) and a convolution attention module. Finally, a lane structural loss is proposed to explicitly model the lane structure and reduce the influence of noise, so that the pixels can fit the lane better. Extensive experimental results demonstrate that the performance of our method is significantly better than the state-of-the-art (SOTA) algorithms such as Pointlanenet and Line-CNN et al. 95.28% and 90.06% accuracy and 62.5 frames per second (fps) inference speed can be achieved on the CULane and Tusimple test dataset. Compared with the latest ERFNet, Line-CNN, SAD, F1 scores have respectively increased by 3.51%, 2.84%, and 3.82%. Meanwhile, the result from our dataset exceeds the top performances of the other by 8.6% with an 87.09 F1 score, which demonstrates the superiority of our method.

SLAM (simultaneous localization and mapping) is becoming a significant technology in driverless vehicles nowadays. Sometimes the optical flow method can be used to track feature points to reduce the amount of calculation. But the traditional optical flow method is based on the assumption that the luminosity will not change and has high requirements for the environment. Therefore, an optical flow calculation method based on neural network called Optical-Net is proposed in this paper to match and track feature and corner points. We use the multi-head attention module to extract and fuse the features of different scales. The training results show that the Optical-Net designed in this study effectively improves the robustness and accuracy of optical flow estimation when the object has large displacement and small displacement. Meanwhile, to address the problem of reduced matching accuracy during video input due to dynamic blurring, a video dynamic deblurring algorithm based on the one-dimensional Wineman filter is adopted to preprocess the input video. Finally, we replaced the tracking thread in traditional SLAM. The performance of the improved algorithm is verified by experiments on our own driverless car.