Papers NavSim
“NavSim” 태그가 달린 논문 26편 · 필터 해제
World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model
End-to-end autonomous driving directly generates planning trajectories from raw sensor data, yet it typically relies on costly perception supervision to extract scene information. A critical research challenge arises: co…
Autonomous DrivingNavSimSelf-Supervised LearningEpona: Autoregressive Diffusion World Model for Autonomous Driving
Diffusion models have demonstrated exceptional visual quality in video generation, making them promising for autonomous driving world modeling. However, existing video diffusion-based world models struggle with flexible-…
Autonomous DrivingmodelMotion PlanningNavSim+3ReSim: Reliable World Simulation for Autonomous Driving
How can we reliably simulate future driving scenarios under a wide range of ego driving behaviors? Recent driving world models, developed exclusively on real-world driving data composed mainly of safe expert trajectories…
Autonomous DrivingNavSimReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
Although end-to-end autonomous driving has made remarkable progress, its performance degrades significantly in rare and long-tail scenarios. Recent approaches attempt to address this challenge by leveraging the rich worl…
Autonomous DrivingImitation LearningNavSimQuestion Answering+1Generalized Trajectory Scoring for End-to-end Multimodal Planning
End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory scorer capable of selecting the optimal t…
Autonomous DrivingDomain GeneralizationNavSimDriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
In complex driving environments, autonomous vehicles must navigate safely. Relying on a single predicted path, as in regression-based approaches, usually does not explicitly assess the safety of the predicted trajectory.…
Autonomous VehiclesCollision AvoidanceNavigateNavSimPseudo-Simulation for Autonomous Driving
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can f…
Autonomous DrivingAutonomous VehiclesNavSimGaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving
Multi-sensor fusion is crucial for improving the performance and robustness of end-to-end autonomous driving systems. Existing methods predominantly adopt either attention-based flatten fusion or bird's eye view fusion t…
Autonomous DrivingBench2DriveNavSimSensor Fusion+1iPad: Iterative Proposal-centric End-to-End Autonomous Driving
End-to-end (E2E) autonomous driving systems offer a promising alternative to traditional modular pipelines by reducing information loss and error accumulation, with significant potential to enhance both mobility and safe…
Autonomous DrivingBench2DriveNavSimTransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving
In recent years, diffusion model has shown its potential across diverse domains from vision generation to language modeling. Transferring its capabilities to modern autonomous driving systems has also emerged as a promis…
Autonomous DrivingDecoderDenoisingLanguage Modeling+3End-to-End Driving with Online Trajectory Evaluation via BEV World Model
End-to-end autonomous driving has achieved remarkable progress by integrating perception, prediction, and planning into a fully differentiable framework. Yet, to fully realize its potential, an effective online trajector…
Autonomous DrivingBench2DriveNavSimHydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
End-to-end autonomous driving research currently faces a critical challenge in bridging the gap between open-loop training and closed-loop deployment. Current approaches are trained to predict trajectories in an open-loo…
Autonomous DrivingBench2DriveNavSimTrajectory PredictionCentaur: Robust End-to-End Autonomous Driving with Test-Time Training
How can we rely on an end-to-end autonomous vehicle's complex decision-making system during deployment? One common solution is to have a ``fallback layer'' that checks the planned trajectory for rule violations and repla…
Autonomous DrivingNavSimFinetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback
Generating human-like and adaptive trajectories is essential for autonomous driving in dynamic environments. While generative models have shown promise in synthesizing feasible trajectories, they often fail to capture th…
Autonomous DrivingImitation LearningMotion PlanningNavSimGoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increas…
Autonomous DrivingDenoisingNavSimDrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers
World model-based searching and planning are widely recognized as a promising path toward human-level physical intelligence. However, current driving world models primarily rely on video diffusion models, which specializ…
NavSimTrajectory PlanningVideo GenerationBench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model
For end-to-end autonomous driving (E2E-AD), the evaluation system remains an open problem. Existing closed-loop evaluation protocols usually rely on simulators like CARLA being less realistic; while NAVSIM using real-wor…
Autonomous DrivingBench2DriveNavSimDiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capability for end-to-end autonomous driving i…
Autonomous DrivingDenoisingNavSimNAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
Benchmarking vision-based driving policies is challenging. On one hand, open-loop evaluation with real data is easy, but these results do not reflect closed-loop performance. On the other, closed-loop evaluation is possi…
Autonomous DrivingBenchmarkingNavSimEnhancing End-to-End Autonomous Driving with Latent World Model
End-to-end autonomous driving has garnered widespread attention. Current end-to-end approaches largely rely on the supervision from perception tasks such as detection, tracking, and map segmentation to aid in learning sc…
Autonomous DrivingNavSim