arXiv Computer Vision
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
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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
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
Jul 7
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.
arXiv AI
Sep 3
DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space
DiffuSearch is a hybrid trajectory planner for autonomous driving that unifies objectives across both generation and refinement stages. It first uses a guided diffusion model to produce scene-consistent joint trajectories, then refines them with a Monte Carlo Tree Search that shares the same driving goals—collision avoidance, drivable area compliance, comfort, and progress. Experiments on nuPlan and interPlan benchmarks show that this synergy reduces collisions and improves comfort, especially in complex interactive scenarios.
arXiv AI
Jul 2
What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models
arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.
arXiv AI
Jul 1
What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
arXiv:2606. 31106v1 Announce Type: cross Abstract: Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal scenarios can still mask flawed reasoning and unsafe heuristics.