GEM represents driving scenes as explicit continuous 4D Gaussian primitives with learned dynamics to enable direct querying at arbitrary timestamps for semantic occupancy forecasting and motion planning.
Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline.Advances in Neural Information Processing Systems, 35:6119–6132
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MDrive benchmark shows multi-agent cooperative driving systems generally outperform single-agent ones in closed-loop settings but perception sharing does not always improve planning and negotiation can harm performance in complex traffic.
citing papers explorer
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GEM: Gaussian Evolution Model for Occupancy Forecasting and Motion Planning
GEM represents driving scenes as explicit continuous 4D Gaussian primitives with learned dynamics to enable direct querying at arbitrary timestamps for semantic occupancy forecasting and motion planning.
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MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
MDrive benchmark shows multi-agent cooperative driving systems generally outperform single-agent ones in closed-loop settings but perception sharing does not always improve planning and negotiation can harm performance in complex traffic.