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.
Transfuser: Imitation with transformer-based sensor fusion for au- tonomous driving
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UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
MTA-RL predicts 3D driving affordances from multi-modal sensors with a transformer and uses them as the observation space for an RL policy, yielding better route completion and generalization than baselines in CARLA urban 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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UniUncer: Unified Dynamic Static Uncertainty for End to End Driving
UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
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MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving
MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
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MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning
MTA-RL predicts 3D driving affordances from multi-modal sensors with a transformer and uses them as the observation space for an RL policy, yielding better route completion and generalization than baselines in CARLA urban scenarios.