无人车综述：Abstract— We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision- making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the UFES’s car, IARA. Finally, we list prominent autonomous research cars developed by technology companies and reported in the media

北京技术峰会，利用 AWS 云端 GPU 服务，Momenta 构建自动驾驶数据筛选训练平台。

驾驶员仿真平台和台架应用

中国智能交通车路协同的高等级自动驾驶数据交互内容-PDF.7z

自主汽车 硬件平台： 计算平台 杰特逊TX2 arduino-uno 车载平台 遥控车 传感器 velodyne-16 Razor-9dof-imu

1、自动驾驶 2、V2X 3、RSU 4、车路协同

汽车自动驾驶用胶粘剂行业调研

模仿学习 该存储库提供了论文的Tensorflow实现。 您可以找到经过预训练的网络。 当前的存储库添加了Tensorflow培训代码。 本文中的设置只有几处更改： 我们训练的步数更少（我们执行190k步，本文执行450k步），但这是可配置的。 控制器的分支遵循训练数据的顺序。 我们对输出采用不同的权重超参数（转向，汽油，刹车，速度），因为本文中建议的超参数对我们不起作用。 设置 该存储库使用docker映像。 为了使用它，请安装 。 要生成图像，请使用： docker build --build-arg base_image=tensorflow/tensorflow:1.12.0-gpu -t imit-learn . 如果仅需要CPU映像，请base_image=tensorflow/tensorflow:1.12.0-gpu 。 到目前为止，我们仅使用tensorf

huawei的MDC培训教材（pdf+doc）+实验环境搭建指导（pdf+ppt）+自动驾驶部署指南（pdf）共计5个文档，及EM源码

自动驾驶中的毫米波雷达信号处理