MDrive benchmark shows multi-agent cooperative driving systems generally outperform single-agent ones in closed-loop settings but perception sharing does not always improve planning and negotiation can harm performance in complex traffic.
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nuPlan: A closed-loop ML- based planning benchmark for autonomous vehicles
17 Pith papers cite this work. Polarity classification is still indexing.
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CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
C-TRAIL combines LLM commonsense with a dual-trust mechanism and Dirichlet-weighted Monte Carlo Tree Search to improve trajectory planning accuracy and safety in autonomous driving.
Smaller end-to-end autonomous driving models achieve optimal 3-second trajectory prediction accuracy at lower or intermediate temporal sampling frequencies, whereas larger VLA-style models perform best at the highest frequencies across Waymo, nuScenes, and PAVE datasets.
DriveFuture achieves SOTA results on NAVSIM by conditioning latent world model states on future predictions to directly inform trajectory planning.
SceneFactory delivers a batched GPU platform for physics-based multi-agent autonomous driving simulation that achieves 127x higher throughput than non-vectorized PhysX while supporting articulated dynamics and road-condition friction.
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
ReflectDrive-2 combines masked discrete diffusion with RL-aligned self-editing to generate and refine driving trajectories, reaching 91.0 PDMS on NAVSIM camera-only and 94.8 in best-of-6.
ProDrive couples a query-centric planner with a BEV world model for end-to-end ego-environment co-evolution, enabling future-outcome assessment that improves safety and efficiency over reactive baselines on NAVSIM v1.
OneDrive unifies heterogeneous decoding in a single VLM transformer decoder for end-to-end driving, achieving 0.28 L2 error and 0.18 collision rate on nuScenes plus 86.8 PDMS on NAVSIM.
Mosaic integrates rule-based and learned planners via arbitration graphs to set new state-of-the-art scores on nuPlan and interPlan benchmarks while cutting at-fault collisions by 30%.
The primary OL-CL gap in end-to-end autonomous driving arises from objective mismatch creating structural inability to model reactive behaviors, which a test-time adaptation method can mitigate.
E² uses transport-regularized sparse control on learned reverse-time SDEs with topology-driven selection and Topological Anchoring to generate realistic adversarial scenarios, improving collision discovery by 9.01% on nuScenes and up to 21.43% on nuPlan while enabling closed-loop robustness gains.
Hydra-MDP uses multi-teacher distillation and a multi-head decoder to learn diverse, metric-specific trajectories in an end-to-end autonomous-driving planner, winning the Navsim challenge.
CaAD adds ego-centric joint-causal modeling and causality-aware policy alignment to end-to-end driving, reporting Driving Score 87.53 and Success Rate 71.81 on Bench2Drive plus PDMS 91.1 on NAVSIM.
This survey synthesizes AI techniques for mixed autonomy traffic simulation and introduces a taxonomy spanning agent-level behavior models, environment-level methods, and cognitive/physics-informed approaches.
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SceneFactory: GPU-Accelerated Multi-Agent Driving Simulation with Physics-Based Vehicle Dynamics
SceneFactory delivers a batched GPU platform for physics-based multi-agent autonomous driving simulation that achieves 127x higher throughput than non-vectorized PhysX while supporting articulated dynamics and road-condition friction.