Tata Motors欧洲技术中心通过基于模型的设计加速了自主车辆控制算法的开发

TMETC团队的旨在为一辆具有一小队工程师提供可观的自驾车，同时按计划和预算保持项目。为了满足这些目标，它们可以在可能的情况下依赖于现成的组件，并寻找缩短核心控制算法的开发时间的方法。

主要设计挑战是集成了系统的许多不同元素。这些元素包括雷达，激光雷达，GPS，惯性测量和单声道，以及传感器融合，运动规划，同时定位和映射的算法和车辆控制。

All communication between elements had to be logged to comply with UK regulations, particularly “The Pathway to Driverless Cars: A Code of Practice for Testing,” published by the Department of Transport. The team decided to use the Robot Operating System (ROS) middleware to address integration and logging requirements. As a result, the algorithms they wrote needed ROS interfaces, and the team needed a way to visualize and analyze logged ROS data.

TMERC的工程师使用Simulink来模拟，金宝app模拟和生成用于在自主Hexa中部署的运动规划和车辆控制算法的代码。

Three vehicle control algorithms were developed: pure pursuit, lane keeping, and model predictive control. To evaluate each algorithm, they integrated it with simple lateral and longitudinal models of the vehicle and ran closed-loop simulations.

缺乏足够的stabil的纯粹追求方法ity, and the lane-keeping approach performed relatively poorly in urban centers that required navigation of tight curves and slow speeds. The model predictive controller performed well in simulations spanning a range of operating scenarios.

该团队通过参考设定点，车辆动态测量和车辆动力学模型来精制横向和纵向模型预测控制器，以产生用于转向，加速和制动的最佳车辆控制序列，以便遵循计划的轨迹。

使用硬件循环测试来检查硬件接口。

The TMETC team generated code from their motion planning algorithms with Embedded Coder^®and deployed it to a Linux-based PC installed in the vehicle. Using Simulink Real-Time™, they deployed their vehicle control algorithms tospeedgoat.target hardware installed in the vehicle.

On-road tests were conducted, during which data was logged from ROS data as well as directly from the vehicle controller. Data was analyzed and visualized using RViz, MATLAB, and Robotics System Toolbox™. To debug and further refine the control algorithms, logged driving scenarios data was played back through the controllers in simulation.

TMERC成功地证明了他们在英国汽车速度和米尔顿凯恩斯的英国自动渗透项目的车辆试验中的城市道路和网桥街道的混合中展示了自主车辆。

• 实时控制器实现加速。“As soon as we were ready for testing on the vehicle, we used Simulink Real-Time to deploy our vehicle controller to the Speedgoat hardware,” says Tucker.
• 调试简化。“金宝appSimulink使我们能够在仿真中播放从道路测试的数据，”Tucker说。“我们可以在任何时候停止模拟，使得可以挖掘控制模型，以查看正在发生的事情并解决我们在算法中所识别的任何怪癖。”
• Development time focused on design.“All the motion planning and vehicle control code was generated from our Simulink models,” says Tucker. “This saved us a lot of time because we could concentrate on the high-level design, not implementing equations and handling exceptions in code. Coding our control algorithms by hand would have been a much larger task.”