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.
Hugsim: A real-time, photo-realistic and closed-loop simulator for autonomous driving
8 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
Fail2Drive is the first paired-route benchmark for closed-loop generalization in CARLA, showing an average 22.8% success-rate drop on shifted scenarios and revealing failure modes such as ignoring visible LiDAR objects.
P2GS jointly decomposes LDR images into a view-invariant linear HDR radiance field, per-view exposure scales, and tone-mapping functions without HDR supervision to enforce photometric consistency in urban Gaussian Splatting.
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.
DriveLaW unifies video world modeling and trajectory planning by injecting video-generator latents into a diffusion planner, achieving SOTA video prediction and a new record on the NAVSIM planning benchmark.
SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.
RAD-2 uses a diffusion generator and RL discriminator to cut collision rates by 56% in closed-loop autonomous driving planning.
citing papers explorer
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MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
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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Fail2Drive: Benchmarking Closed-Loop Driving Generalization
Fail2Drive is the first paired-route benchmark for closed-loop generalization in CARLA, showing an average 22.8% success-rate drop on shifted scenarios and revealing failure modes such as ignoring visible LiDAR objects.
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P2GS: Physical Prior-guided Gaussian Splatting for Photometrically Consistent Urban Reconstruction
P2GS jointly decomposes LDR images into a view-invariant linear HDR radiance field, per-view exposure scales, and tone-mapping functions without HDR supervision to enforce photometric consistency in urban Gaussian Splatting.
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BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving
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.
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DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
DriveLaW unifies video world modeling and trajectory planning by injecting video-generator latents into a diffusion planner, achieving SOTA video prediction and a new record on the NAVSIM planning benchmark.
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SimScale: Learning to Drive via Real-World Simulation at Scale
SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
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VERDI: VLM-Embedded Reasoning for Autonomous Driving
VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.
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RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
RAD-2 uses a diffusion generator and RL discriminator to cut collision rates by 56% in closed-loop autonomous driving planning.