arXiv Machine Learning
EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan
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DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance
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AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
arXiv:2603. 28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems.
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LIDAR-AD: A Decoder-Free Latent-Interaction Dreamer with Action-Residual Chains for Autonomous Driving
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LIDAR-AD: A Decoder-Free Latent-Interaction Dreamer with Action-Residual Chains for Autonomous Driving
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DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle
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The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
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