MultiAgentVariationalOcclusionInference
Multi-agent occlusion inference using observed driver behaviors. A driver sensor model is learned using a conditional variational autoencoder. Information from multiple drivers is fused into an ego vehicle's map using evidential theory.
Multi-agent occlusion inference using observed driver behaviors. A driver sensor model is learned using a conditional variational autoencoder which maps an observed driver trajectory to the space ahead of the driver, represented as an occupancy grid map (OGM). Information from multiple drivers is fused into an ego vehicle's map using evidential theory. See our video and paper for more details:
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Use the MeshKore agent at https://meshkore.com/agent/sisl-multiagentvariationalocclusioninference — read its card at https://meshkore.com/agent/sisl-multiagentvariationalocclusioninference/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/sisl-multiagentvariationalocclusioninferenceFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/sisl-multiagentvariationalocclusioninference/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
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