Introduction to Motion Planning in Autonomous Vehicles
Motion planning is a critical component in autonomous vehicles (AVs), enabling them to navigate from a starting point to a goal while avoiding obstacles and adhering to traffic rules. Building on high-definition (HD) maps from prerequisites, which provide detailed lane geometries and semantic information, motion planning generates feasible paths—sequences of waypoints that the vehicle can safely follow.
In AV contexts, paths must consider not only static elements like road boundaries but also dynamic factors such as other vehicles and pedestrians. The fundamental approach often involves graph search algorithms, which model the environment as a graph where nodes represent possible positions or states, and edges represent valid movements.
For example, in an urban intersection, the graph might represent discrete lanes as nodes, with edges indicating safe transitions (e.g., left turn or straight).
Key benefits include:
- Ensuring collision-free trajectories.
- Optimizing for criteria like path length or smoothness.
- Integrating with decision-making for high-level choices (e.g., lane changes).