4DLidarOpen is a new open dataset providing synchronized 4D FMCW Lidar velocity measurements, multi-Lidar and camera data, and 3D bounding-box annotations with track IDs to support benchmarks on 3D detection, BEV segmentation, flow prediction, and motion forecasting.
A survey of motion planning and control techniques for self-driving urban vehicles,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2representative citing papers
Graphs of convex sets with Bezier paths and a simplified bicycle model produce trajectories that closely match nonlinear optimal control results but with better speed and initialization robustness in CommonRoad driving scenarios.
citing papers explorer
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4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving
4DLidarOpen is a new open dataset providing synchronized 4D FMCW Lidar velocity measurements, multi-Lidar and camera data, and 3D bounding-box annotations with track IDs to support benchmarks on 3D detection, BEV segmentation, flow prediction, and motion forecasting.
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Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets
Graphs of convex sets with Bezier paths and a simplified bicycle model produce trajectories that closely match nonlinear optimal control results but with better speed and initialization robustness in CommonRoad driving scenarios.