User:Phsin1129/Self-driving car

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Path planning

Path planning is a computational problem to find a sequence of valid configurations that moves the object from the source to destination. The large scale path of the vehicle can be determined by using a voronoi diagram, an occupancy grid mapping, or with a driving corridors algorithm. However, these traditional approaches are not sufficient for a vehicle that is interacting with other moving objects, and several advanced approaches applying machine learning are under developments.

A driving corridors algorithm allows the vehicle to locate and drive within open free space that is bounded by lanes or barriers.

Self-driving cars rely on path planning technology in order to follow the rules of traffic and prevent accidents from occurring. While these algorithms work in a simple situation, path planning has not been proven to be effective in a complex scenario.

Two techniques used for path planning are Graph-based search and variational-based optimization techniques. Graph-based techniques can make harder decisions such as how to pass another vehicle/obstacle. Variational-based optimization techniques require a higher level of planning in setting restrictions on the vehicle's driving corridor to prevent collisions.

Sensor fusion

Control systems on automated cars may use sensor fusion, which is an approach that integrates information from a variety of sensors on the car to produce a more consistent, accurate, and useful view of the environment.

Self-driving cars tend to use a combination of cameras, LiDAR sensors, and radar sensors in order to enhance performance and ensure the safety of the passenger and other drivers on the road. An increased consistency in self-driving performance prevents accidents that may occur because of one faulty sensor.