arXiv:2606. 15930v1 Announce Type: cross Abstract: Simulation is central to validating autonomous driving systems, yet current pipelines are limited by insufficient scenario diversity due to costly High Definition (HD) map creation.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
arXiv:2606. 04513v1 Announce Type: new Abstract: Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive.
arXiv:2608. 11580v1 Announce Type: cross Abstract: Autonomous driving simulation requires diverse and scalable lane-level HD maps to support long-horizon evaluation across complex road networks.
arXiv:2607. 10565v1 Announce Type: cross Abstract: End-to-end motion planning has emerged as a promising paradigm in autonomous driving, directly mapping raw sensor data to control commands via deep neural networks.
arXiv:2606. 02956v1 Announce Type: cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity.
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class of models includes both Vision-Language-Action models and trajectory-generative planners.