Motion prediction is critical for autonomous off-road driving, however, it
presents significantly more challenges than on-road driving because of the
complex interaction between the vehicle and the terrain. Traditional
physics-based approaches encounter difficulties in accurately modeling dynamic
systems and external disturbance. In contrast, data-driven neural networks
require extensive datasets and struggle with explicitly capturing the
fundamental physical laws, which can easily lead to poor generalization. By
merging the advantages of both methods, neuro-symbolic approaches present a
promising direction. These methods embed physical laws into neural models,
potentially significantly improving generalization capabilities. However, no
prior works were evaluated in real-world settings for off-road driving. To
bridge this gap, we present PhysORD, a neural-symbolic approach integrating the
conservation law, i.e., the Euler-Lagrange equation, into data-driven neural
models for motion prediction in off-road driving. Our experiments showed that
PhysORD can accurately predict vehicle motion and tolerate external disturbance
by modeling uncertainties. It outperforms existing methods both in accuracy and
efficiency and demonstrates data-efficient learning and generalization ability
in long-term prediction.

通过将保守定律嵌入到数据驱动的神经模型中，我们提出了 PhysORD，一种用于越野驾驶中运动预测的神经符号方法。我们的实验证明，PhysORD 可以通过建模不确定性准确地预测车辆运动，并且可以容忍外部干扰。它在准确性和效率方面优于现有方法，并展示了长期预测中的数据效率学习和泛化能力。

PhysORD: 基于神经符号方法的越野驾驶中融合物理的运动预测

PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction  in Off-road Driving

Recently, medical report generation, which aims to automatically generate a
long and coherent descriptive paragraph of a given medical image, has received
growing research interests. Different from the general image captioning tasks,
medical report generation is more challenging for data-driven neural models.
This is mainly due to 1) the serious data bias: the normal visual regions
dominate the dataset over the abnormal visual regions, and 2) the very long
sequence. To alleviate above two problems, we propose an AlignTransformer
framework, which includes the Align Hierarchical Attention (AHA) and the
Multi-Grained Transformer (MGT) modules: 1) AHA module first predicts the
disease tags from the input image and then learns the multi-grained visual
features by hierarchically aligning the visual regions and disease tags. The
acquired disease-grounded visual features can better represent the abnormal
regions of the input image, which could alleviate data bias problem; 2) MGT
module effectively uses the multi-grained features and Transformer framework to
generate the long medical report. The experiments on the public IU-Xray and
MIMIC-CXR datasets show that the AlignTransformer can achieve results
competitive with state-of-the-art methods on the two datasets. Moreover, the
human evaluation conducted by professional radiologists further proves the
effectiveness of our approach.

该研究提出了一个包含 Align Hierarchical Attention (AHA) 和 Multi-Grained Transformer (MGT) modules 在内的 AlignTransformer 框架，可以用于医疗图像的自动报告生成，实验表明该方法可以解决数据偏差问题，并取得了与现有最佳方法相竞争的结果。