ChatGPT, a chatbot launched by OpenAI, is making waves across the internet recently. Its potential capabilities and how it can impact various industries have been widely discussed. In the field of smart vehicles, integrating human-machine interaction systems with ChatGPT can be a game changer. For example, Jidu Auto, the electric vehicle arm of Baidu, announced in February 2023 that they will integrate ERNIE Bot [1], Baidu's ChatGPT-like service, into their cars. While ChatGPT has boosted the development of intelligent interaction, some limitations still exist that hinder its practical application. In this article, we discuss its potential applications in intelligent vehicles and analyze the challenges it faces.

Given the precalibrated image pairs, stereo matching aims to infer the scene depth information in real-time, which has important research value in the fields of high-precision 3-D reconstruction of the Earth’s surface, automatic driving and unmanned aerial vehicle (UAV) navigation. The cost volume-based stereo matching method adopts a coarse-to-fine manner to construct cascaded cost volume, and applies 3-D convolution to capture the correspondence of feature matching to infer the disparity map, which achieves comparable performance. However, the existing method has difficulty dealing with jitter regions with disparity change, and direct disparity regression easily leads to overfitting of cost volume regularization. To alleviate the above two problems, this work proposes an end-to-end disparity estimation network based on Transformer. Its specific improvements are as follows. 1) The cross-view feature interaction module based on Transformer is introduced to realize the feature interaction of global context information. 2) A parallax attention mechanism is designed to impose global geometric constraints on the epipolar line to improve the reliability of feature matching. 3) Focal loss is applied for the training of the disparity classification model to emphasize one-hot supervision in ambiguous regions. Comprehensive experiments on public datasets Sceneflow, KITTI2015, ETH3D, and aerial WHU datasets validate that the proposed work can effectively enhance the performance of disparity estimation.

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows.

Multiagent cooperative systems can be used to conceptualize many real-world problems. Reinforcement learning is a particularly effective tool. The issue of bias in Q-function value estimation in single-agent reinforcement learning has garnered a lot of interest and substantial study. Indeed, this challenge endures in multiagent reinforcement learning, primarily owing to the inclusion of maximization operations. The crux of the matter lies in the inability to seamlessly extrapolate single-agent reinforcement learning algorithms to their multiagent counterparts. In this article, we introduce a more encompassing and straightforward principle: the notion of appropriate value correction. We suggest replacing the maximization operation with a monotonically nondecreasing function to obtain more accurate value estimates. We theoretically demonstrate that this operation effectively reduces the potential overestimation bias in the QMIX algorithm. Ultimately, our methodology, dubbed the SMIX algorithm—a fusion of the QMIX algorithm empowered by the Softmax operator, attains state-of-the-art outcomes across diverse multiagent cooperative tasks. This success extends to challenging domains such as StarCraft II, marking it as one of the most formidable games to date.