arXiv AI
Beyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving
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arXiv Computer Vision
Sep 30
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
arXiv:2601.01762v4 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes....
arXiv AI
Jun 6
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
arXiv:2606. 06014v1 Announce Type: new Abstract: Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning.
arXiv AI
Jun 6
CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving
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 AI
Sep 17
Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving
RiskWorld is a risk‑aware world modeling framework that forecasts shared occupancy and selectively replaces planned trajectories in automated driving. It fuses spatial risk fields, temporal actor context, and visual bird’s‑eye‑view features, using flow‑guided evolution to transport occupancy and signed residuals to correct it. In open‑loop planning on nuScenes, RiskWorld achieves the lowest collision rate over a 3‑second horizon and the second‑best average L2 error, running at 11.5 FPS on a single NVIDIA RTX 4090.
arXiv AI
Sep 21
Benchmarking Autonomous Driving Planners Across Leaderboards: A Unified CARLA-Based Evaluation
The paper presents a comparative study of motion planning methods from major autonomous driving benchmarks—CARLA, nuPlan, and the Waymo Open Dataset—using CARLA Leaderboard v2.1 as a unified evaluation platform. Eight representative planners (TF++, InterFuser, TCP, PDM‑Lite, MTR+MPC, CaRL, PlanT 2.0, Diffusion planner) are evaluated to highlight their strengths, weaknesses, prevailing trends, and common challenges in motion planning research.
arXiv AI
Jun 19
Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving
arXiv:2606. 20274v1 Announce Type: new Abstract: Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and planners that produce kinematically valid trajectories.