环境感知是自动驾驶汽车领域中的一个重要方面，它提供了有关驾驶领域的重要信息，包括但不限于确定明确的驾驶区域和周围的障碍。语义分割是一种用于自动驾驶汽车的广泛使用的感知方法，它将图像的每个像素与预定义的类相关联。在这种情况下，评估了有关准确性和效率的几个分割模型。生成数据集的实验结果确认，更快的分割模型足够快，可以实时在自动驾驶汽车中的低力计算（嵌入式）设备上使用。还引入了一种简单的方法来为模型生成合成训练数据。此外，比较了第一人称视角的准确性和鸟类的视角。对于第一人称视角的$ 320 \ times 256 $输入，更宽松的是$ 65.44 \，\％$均值均值的交叉点（miou），以及$ 320 \ times 256 $的输入，从鸟类的眼睛的角度来看，forppective fore fore fore fore fore fore foreveves foreveves fore fore fore fore for。 \，\％$ miou。这两种观点都在Nvidia Jetson Agx Xavier上达到每秒247.11美元的框架率。最后，测量并比较目标硬件的算术速率和相对于算术16位浮点（FP16）和32位浮点（FP32）的精度。

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models