This letter reports on a TIV DHW(decentralized and hybrid workshop) that explores the prospective influence of ChatGPT on research and development in intelligent vehicles. To assess the update capabilities of ChatGPT, we conducted tests involving both basic and technically relevant questions. Our preliminary testing revealed that ChatGPT's information can be updated and corrected at one time, but it may take some time for the changes to be reflected in ChatGPT's responses, so it may not always possess the latest knowledge regarding specific topics. We further discuss the prospective influence of ChatGPT on the field of intelligent vehicles, particularly possible applications of ChatGPT in areas like autonomous driving, human-vehicle interaction, and intelligent transportation systems, highlighting challenges and opportunities associated with these applications. Additionally, we address technical questions, such as the feasibility of training intelligent vehicles using the same methods as ChatGPT and the reflection of the intelligence of intelligent vehicles in the context of human-machine shared control. In conclusion, this letter presents a preliminary exploration of the potential of ChatGPT for intelligent vehicle research, from an IEEE TIV perspective, acknowledging the limitations and uncertainties of this emerging technology.

For autonomous driving, one of the major challenges is to predict pedestrian crossing intention in ego-view. Pedestrian intention depends not only on their intrinsic goals but also on the stimulation of surrounding traffic elements. Considering the influence of other traffic elements on pedestrian intention, recent work introduced more traffic element information into the model to successfully improve performance. However, it is still difficult to effectively capture and fully exploit the potential dynamic spatio-temporal interactions among the target pedestrian and its surrounding traffic elements for accurate reasoning. In this work, inspired by neuroscience that human drivers tend to make continuous sensory-motor driving decisions by progressive visual stimulation, we propose a model termed Progressive Interaction Transformer (PIT) for pedestrian crossing intention prediction. Local pedestrian, global environment, and ego-vehicle motion are considered simultaneously in the proposed PIT. In particular, the temporal fusion block and self-attention mechanism are introduced to jointly and progressively model the dynamic spatio-temporal interactions among the three parties, allowing it to capture richer information and make prediction in a similar way to human drivers. Experimental results demonstrate that PIT achieves higher performance compared with other state-of-the-arts and preserves real-time inference.

Breast cancer diagnosis based on medical imaging necessitates both fine-grained lesion segmentation and disease grading. Although deep learning (DL) offers an emerging and powerful paradigm of feature learning for these two tasks, it is hampered from popularizing in practical application due to the lack of interpretability, generalization ability and large labeled training sets. In this paper, we propose a hierarchical fused model based on deep learning and fuzzy learning to overcome the drawbacks for pixel-wise segmentation and disease grading of mammography breast images. The proposed system consists of a segmentation model (ResU-segNet) and a hierarchical fuzzy classifier (HFC) that is a fusion of interval type-2 possibilistic fuzzy c-means (IT2PFCM) and fuzzy neural network (FNN). The ResU-segNet segments the masks of mass regions from the images through convolutional neural networks, while HFC encodes the features from mass images and masks to obtain the disease grading through fuzzy representation and rule-based learning. Through the integration of feature extraction aided by domain knowledge and fuzzy learning, the system achieves favorable performance in a few-shot learning manner and the deterioration of cross-dataset generalization ability is alleviated. Also, the interpretability is further enhanced. The effectiveness of the proposed system is analyzed on publicly available mammogram database of INbreast and a private database through cross-validation. Thorough comparative experiments are also conducted and demonstrated.