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Adapting to the Traffic Swarm: Swarm Behaviour for Autonomous Cars
Abstract
Swarm behavior can be applied to many aspects of autonomous driving: e.g. localization, perception, path planning or mapping. A reason for this is that from the information observed by swarm members, e.g. the relative position and speed of other cars, further information can be derived. In this chapter the processing pipeline of a “swarm behavior module” is described step by step from selecting and abstracting sensor data to generating a plan – a drivable trajectory – for an autonomous car. Such a swarm-based path planning can play an important role in a scenario where there is a mixture of human drivers and autonomous cars. Experienced human drivers flow with the traffic and adapt their driving to the environment. They do not follow the traffic rules as strictly as computers do, but they are often using common sense. Autonomous cars should not provoke dangerous situations by sticking absolutely to the traffic rules, they must adapt their behavior with respect to the other drivers around them and thus merge with the traffic swarm.
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