Swarm intelligence, compound intelligence, or group intelligence may emerge as an important concept as AI develops toward higher complexity. Whether these terms should be considered synonymous is doubtful.
Compound features in biology are the result of control in differentiation during the development of an organism from a single cell. Compounding in biology is a single function performed across like elements.
Swarms are a result of distinct organisms operating in proximity. Swarming in biology is similarity in complex independent behaviors that appear coordinated but are generally engaged in as a defense from predators.
Group intelligence may be distinct from compound intelligence in that adjacent units may be in agreement or opposition, as can be case with intelligent beings in groups. The agreement in the group that a variety of models is permissible. We call them opinions, but they are distinct matches of models to problems that produce different projections and suggest different selections from among the group's options.
The terms general and strong appear in this question and many others and it appears that, mainly for historical reasons, may continue to frustrate clarity.
All intelligence is general in that it learns a generality from specific experiences and then applies that generality to future scenarios to achieve objectives within those scenarios. What makes the application of the generality intelligent is that it is expected to work because it has been working for similar scenarios.
All intelligence is specific in that it is limited to scope of what generalities have been discovered. Certain techniques are significantly more general that others because they are allegedly domain independent. We call this mathematics.
Consequently, the development of artificial intelligence is not a path from specific to general but one of discovering generalities and applying them to specifics. One could say that the most general thinking of mathematics applied to the computer is the primary activity of applied artificial intelligence, and the capabilities that emerge are a more sophisticated as more sophisticated mathematics is represented in working software and hardware.
The generalities take on greater complexity so that they can apply to a greater number of specific scenarios. That is not a gain in strength but a gain in the breadth of potential application.
Physalia physalis is a symbiotic colony of organisms of four types, the pneumatophore (or float), dactylozooids (long tentacles), gastrozooids (feeding tentacles), and gonozooids which produce reproductive gametes. To avoid breaking the historic conception of animals, these organisms are called polyps. The venom of the colony does not come from any of those organisms but rather from another symbiotic organism, cnidocytes, which attach to the tentacles and are released under strictly controlled conditions.
In addition to all this symbiosis, there are several species of fish that use the colony for shade and protection that the colony lets swim among the tentacles and that have formed partial immunity to the cnidocytes. These complex symbiotic networks are not fully understood, but they seem to operate well and create sustainable inter-specie systems.
It's not likely that the organisms, within their lifetimes adapt or remember, but the DNA of each organism has involved in a way that resembles intelligence in that the colony and its symbionts have adapted to the ocean's surface and its biology.
The idea that the system of the biosphere is the first example of broad intelligence is likely, in that design excellence has emerged from an evolutionary processes. It is not altogether ridiculous to propose that human intelligence is a higher speed approach in neurons to the slower speed of DNA replication in larger organisms. Because of the metabolic requirements of growth slows evolution for larger organisms with a greater cell count, these larger organisms may have needed to develop a way to achieve the nimble adaptivity of their lower ancestors.
Neurology facilitates the approximation of some aspects of evolution and may have been the most attainable natural solution to reacquire nimble adaptivity.
Attempting to apply these various ideas to the current and ongoing development of autonomous vehicles reveals a gap in understanding. We don't yet have the mathematics developed to understand how compound, swarm, or group intelligence can be used in the laboratory to accomplish well defined problems in controlled execution scenarios. That is probably a prerequisite to using these ideas in vehicle control.
The system designs of future cars may be like a colony of independent or semi-independent components that each have a role and purpose. Compound, group, and symbiotic designs are likely to develop. The current activities of vehicles on the road or in the air near airports is like a swarm, so the application of that idea is obvious: Avoid collisions.