PathGAN：基于注意生成对抗网络的局部路径规划
PathGAN: Local Path Planning with Attentive Generative Adversarial Networks
摘要：
为了在没有高清地图的情况下实现自动驾驶，我们提出了一种能够从以自我为中心的图像中为自动驾驶车辆生成多条合理路径的模型。我们的生成模型包括两个神经网络：特征提取网络（FEN）和路径生成网络（PGN）。FEN从以自我为中心的图像中提取有意义的特征，而PGN根据驾驶意图和速度从特征中生成多条路径。为了确保生成的路径是合理的，并与意图一致，我们引入了一个专注的训练和训练它与PGN下生成对抗网络框架。我们还设计了一个位置之间的互动模型的路径和隐藏在位置的意图，并设计了一种新的PGN架构，反映互动模型，从而提高了生成的路径的准确性和多样性。最后，我们介绍了ETRIDriving，这是一个自动驾驶数据集，其中记录的传感器数据被标记为离散的高级驾驶动作，并展示了所提出的模型在准确性和多样性方面对ETRIDriving的最新性能。
英文摘要：
To achieve autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: the feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under generative adversarial networks framework. We also devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model, resulting in the improvement of the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
