EdgeVTP delivers the lowest measured end-to-end latency on Jetson-class platforms while matching or exceeding state-of-the-art accuracy on highway trajectory benchmarks by using bounded graph interactions and a one-shot curve decoder.
Argoverse: 3d tracking and forecasting with rich maps
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LLM-driven multi-planner scheduling framework turns open-ended passenger instructions into safe, traceable control signals for autonomous vehicles while cutting query costs and matching specialized safety levels.
citing papers explorer
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EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications
EdgeVTP delivers the lowest measured end-to-end latency on Jetson-class platforms while matching or exceeding state-of-the-art accuracy on highway trajectory benchmarks by using bounded graph interactions and a one-shot curve decoder.
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Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles
LLM-driven multi-planner scheduling framework turns open-ended passenger instructions into safe, traceable control signals for autonomous vehicles while cutting query costs and matching specialized safety levels.