无人车系统课程

Learn how to install ROS and perform basic and intermediate tasks such as creating visual markers, manipulating reference frames, and troubleshooting modules.

Key Outcomes: You will be familiar with a popular de facto simulator for planning and decision making for robots and self-driving cars. After all the projects, you will be an expert on using ROS for developing planning and decision making modules for self-driving cars.

Develop key foundational planning algorithms that are based on A* to navigate in a discretized known and unknown maps.

Key Outcomes: You will be proficient with the A* search algorithm and its application in self-driving car applications. You will also more easily and better understand more advanced cutting-edge algorithms, as A* forms the foundations for those planning and decision-making algorithms.

Develop planner to navigate through multi-lane highway with multiple static obstacles; design motion primitives to allow efficient transition between lanes and fast planning times.

Key Outcomes: You will be experienced in enhancing and adapting the A* algorithm, and successfully implementing a planning module that enables a self-driving car to change lanes while taking into account other, currently static, vehicles on the highway.

Improve your previous planner to handle dynamic obstacles with known trajectories; Safely plan trajectories in the presence of faster and slower vehicles.

Key Outcomes: You will have adapted your planner to take into account other vehicles that are now moving at varying speeds on the highway, overtaking slower vehicles and moving away from faster vehicles.