Falcone的2007年博士论文，主要讲的是无人驾驶MPC控制原理和应用，是无人驾驶MPC控制理论的学习参考。 之前在网上找了挺久的，现在终于找到了，分享给大家学习

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

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.

Falcone的博士论文，主要介绍了自动驾驶车辆模型预测控制。学习过车辆自动驾驶控制方向的，都知道车辆模型预测控制是他在这个领域的开山之作。 很多资源是假的，这个是真的。

基于相机的2D特征跟踪 这是功能跟踪模块，用于评估自动车辆碰撞检测系统的各种检测器/描述符组合。 该项目包括四个部分： 加载图像，设置数据结构并将所有内容放入环形缓冲区以优化内存负载。 集成了几个关键点检测器，例如HARRIS，FAST，BRISK和SIFT，并就关键点数量和速度进行了比较。 使用蛮力以及FLANN方法提取和匹配描述符。 以不同的组合测试各种算法，并就某些性能指标进行比较。 本地运行的依赖项 cmake> = 2.8 所有操作系统： make> = 4.1（Linux，Mac），3.81（Windows） Linux：大多数Linux发行版默认都安装了make Mac： Windows： OpenCV> = 4.1 必须使用-D OPENCV_ENABLE_NONFREE=ON cmake标志从源代码进行编译，以测试SIFT和SURF检测器。 Open

关于自动驾驶汽车非线性模型预测控制算法的论文，作者为Falcone，龚建伟《无人驾驶模型预测控制》一书中参考了该论文的方法