论文电子档

Global-Guide-to-Autonomous-Vehicles-2021（自动驾驶汽车2021年全球指南）.pdf

Autonomous Vehicle Implementation Predictions（自动驾驶汽车的实施预测对交通规划的影响）.pdf

This edited volume includes thoroughly collected on sensing and control for autonomous vehicles. Guidance, navigation and motion control systems for autonomous vehicles are increasingly important in land-based, marine and aerial operations. Autonomous underwater vehicles may be used for pipeline inspection, light intervention work, underwater survey and collection of oceanographic/biological data. Autonomous unmanned aerial systems can be used in a large number of applications such as inspection, monitoring, data collection, surveillance, etc. At present, vehicles operate with limited autonomy and a minimum of intelligence. There is a growing interest for cooperative and coordinated multi-vehicle systems, real-time re-planning, robust autonomous navigation systems and robust autonomous control of vehicles. Unmanned vehicles with high levels of autonomy may be used for safe and efficient collection of environmental data, for assimilation of climate and environmental models and to complement global satellite systems. The target audience primarily comprises research experts in the field of control theory, but the book may also be beneficial for graduate students.

机器学习外文文献

This book describes different methods that are relevant to the development and testing of control algorithms for advanced driver assistance systems (ADAS) and automated driving functions (ADF). These control algorithms need to respond safely, reliably and optimally in varying operating conditions. Also, vehicles have to comply with safety and emission legislation. The text describes how such control algorithms can be developed, tested and verified for use in real-world driving situations. Owing to the complex interaction of vehicles with the environment and different traffic participants, an almost infinite number of possible scenarios and situations that need to be considered may exist. The book explains new methods to address this complexity, with reference to human interaction modelling, various theoretical approaches to the definition of real-world scenarios, and with practically-oriented examples and contributions, to ensure efficient development and testing of ADAS and ADF. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions is a collection of articles by international experts in the field representing theoretical and application-based points of view. As such, the methods and examples demonstrated in the book will be a valuable source of information for academic and industrial researchers, as well as for automotive companies and suppliers.

Waymo打开数据集 Waymo Open数据集于2019年8月首次推出，其感知数据集包括高分辨率传感器数据和1,950个细分的标签。 我们已公开发布Waymo开放数据集，以帮助研究社区在机器感知和自动驾驶技术方面取得进步。 2021年3月更新 我们扩展了Waymo开放数据集，使其还包括一个运动数据集，该运动数据集包含对象轨迹和超过100,000个细分的相应3D地图。 我们已经更新了此存储库，以添加对此新数据集的支持。 请参考。 此外，我们添加了有关实时检测挑战的说明和示例。 请按照以下。 网站 要了解有关数据集的更多信息并访问它，请访问 。 内容 此代码存储库包含： 数据集格式的定义 评估指标 TensorFlow中的Helper功能可帮助构建模型 请参考。 执照 此代码存储库（不包括third_party）已根据Apache许可2.0版获得许可。 出现在third_party中的代

Autonomous-Car-master.zip

In this thesis we consider the problem of designing and implementing Model Predictive Controllers (MPC) for stabilizing the dynamics of an autonomous ground vehicle. For such a class of systems, the non-linear dynamics and the fast sampling time limit the real-time implementation of MPC algorithms to local and linear operating regions. This phenomenon becomes more relevant when using the limited computational resources of a standard rapid prototyping system for automotive applications. In this thesis we first study the design and the implementation of a nonlinear MPC controller for an Active Font Steering (AFS) problem. At each time step a trajectory is assumed to be known over a finite horizon, and the nonlinear MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We demonstrate that experimental tests can be performed only at low vehicle speed on a dSPACE rapid prototyping system with a frequency of 20 Hz. Then, we propose a low complexity MPC algorithm which is real-time capable for wider operating range of the state and input space (i.e., high vehicle speed and large slip angles). The MPC control algorithm is based on successive on-line linearizations of the nonlinear vehicle model (LTV MPC). We study performance and stability of the proposed MPC scheme. Performance is improved through an ad hoc stabilizing state and input constraints arising from a careful study of the vehicle nonlinearities. The stability of the LTV MPC is enforced by means of an additional convex constraint to the finite time optimization problem. We used the proposed LTV MPC algorithm in order to design AFS controllers and combined steering and braking controllers. We validated the proposed AFS and combined steering and braking MPC algorithms in real-time, on a passenger vehicle equipped with a dSPACE rapid prototyping system. Experiments have been performed in a testing center equipped with snowy and icy tracks. For both controllers we showed that vehicle stabilization can be achieved at high speed (up to 75 Kph) on icy covered roads. This research activity has been supported by Ford Research Laboratories, in Dearborn, MI, USA.

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