Pedestrian behavior prediction is essential to enable safe and efficient driving of intelligent vehicles on urban traffic environment. This study presents a novel framework for pedestrian trajectory prediction, which integrates Dynamic Bayesian Networks (DBN) and Sequence-to-Sequence (Seq2Seq) model through an adaptive online weighting method. DBN utilizes environmental features and kinematic information to infer the pedestrian's motion intentions through probabilistic reasoning. Seq2Seq views trajectory predictions as sequence generation tasks, in which the future trajectories are generated relying on the observed trajectories. A real-world pedestrian motion dataset is employed for model validations and it is also enlarged through data augmentation techniques to enable training of data-driven approaches. We compare our model with several typical baselines methods and results show that our model outperforms those baselines. The average values of the mean Euclidean distance error and the final destination Euclidean distance error with one-second prediction are 0.04m and 0.10m in crossing scenarios, and 0.06m and 0.17m in stopping scenarios, respectively. The study expects to provide a basis for decision-making of intelligent vehicles in order to protect vulnerable road users

Acquiring large amounts of high-quality real sonar data for object detection of autonomous underwater vehicles (AUVs) is challenging. Synthetic data can be an alternative, but it is hard to generate diverse data using traditional generative models when real data are limited. This study proposes a novel style transfer method, i.e., the multigranular feature alignment cycle-consistent generative adversarial network (CycleGAN), to generate sonar images leveraging remote sensing images, which can alleviate the dependence on real sonar data. Specifically, we add a spatial attention-based feature aggregation module to preserve unique features by attending to instance parts of an image. A pair of cross-domain discriminators are designed to guide generators to produce images that capture sonar styles. We also introduce a novel cycle consistency loss based on the discrete cosine transform of images, which better utilizes features that are evident in the frequency domain. Extensive experimental results show that the generated sonar images have better quality than CycleGAN, with improvements of 15.2% in IS, 56.9% in FID, 42.6% in KID, and 7.6% in learned perceptual image patch similarity, respectively. Moreover, after expanding the real sonar dataset with generated data, the average accuracy of the object detector, e.g., YOLOv6, has increased by more than 48.7%, indicating the effectiveness of the generated sonar data by our method.

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented to help follower agents acquire knowledge and experience from a single well-trained pioneer agent. Notably, the designed approach can transfer both the policy representation and sample experience provided by the pioneer policy in the off-policy learning. More importantly, the proposed method can adaptively adjust the learning weight of prior experience and exploration according to the Wasserstein distance between the policy probability distributions of the pioneer and the follower. Case studies show that the distributed agents trained by the proposed method can complete a collaborative task and acquire the maximum rewards while minimizing the violation of constraints. Moreover, the proposed method can also achieve satisfactory performance in terms of learning speed and success rate.