Papers Bench2Drive
“Bench2Drive” 태그가 달린 논문 35편 · 필터 해제
GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving
End-to-end autonomous driving requires adaptive and robust handling of complex and diverse traffic environments. However, prevalent single-mode planning methods attempt to learn an overall policy while struggling to acqu…
Autonomous DrivingBench2DriveMixture-of-ExpertsFocalAD: Local Motion Planning for End-to-End Autonomous Driving
In end-to-end autonomous driving,the motion prediction plays a pivotal role in ego-vehicle planning. However, existing methods often rely on globally aggregated motion features, ignoring the fact that planning decisions …
Autonomous DrivingBench2DriveMotion Planningmotion predictionFrom Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving
Ensuring robust and generalizable autonomous driving requires not only broad scenario coverage but also efficient repair of failure cases, particularly those related to challenging and safety-critical scenarios. However,…
Autonomous DrivingBench2DriveDiversityCogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
While end-to-end autonomous driving has advanced significantly, prevailing methods remain fundamentally misaligned with human cognitive principles in both perception and planning. In this paper, we propose CogAD, a novel…
Autonomous DrivingBench2DriveGaussianFusion: 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+1ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
Due to the powerful vision-language reasoning and generalization abilities, multimodal large language models (MLLMs) have garnered significant attention in the field of end-to-end (E2E) autonomous driving. However, their…
Autonomous DrivingBench2DriveImitation LearningZero-shot GeneralizationRaw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E-AD) remains an open problem for its tra…
Autonomous DrivingBench2DriveCARLA Leaderboard 2.0Imitation Learning+4DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
End-to-end autonomous driving (E2E-AD) demands effective processing of multi-view sensory data and robust handling of diverse and complex driving scenarios, particularly rare maneuvers such as aggressive turns. Recent su…
Autonomous DrivingBench2DriveMixture-of-ExpertsVision-Language-ActioniPad: 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 DrivingBench2DriveNavSimSafety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving
Autonomous Driving (AD) systems demand the high levels of safety assurance. Despite significant advancements in AD demonstrated on open-source benchmarks like Longest6 and Bench2Drive, existing datasets still lack regula…
Autonomous DrivingBench2DriveLane Detectionobject-detection+1X-Driver: Explainable Autonomous Driving with Vision-Language Models
End-to-end autonomous driving has advanced significantly, offering benefits such as system simplicity and stronger driving performance in both open-loop and closed-loop settings than conventional pipelines. However, exis…
Autonomous DrivingBench2DriveDecision MakingTwo Tasks, One Goal: Uniting Motion and Planning for Excellent End To End Autonomous Driving Performance
End-to-end autonomous driving has made impressive progress in recent years. Former end-to-end autonomous driving approaches often decouple planning and motion tasks, treating them as separate modules. This separation ove…
Autonomous DrivingBench2DriveEnd-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 DrivingBench2DriveNavSimORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation
End-to-end (E2E) autonomous driving methods still struggle to make correct decisions in interactive closed-loop evaluation due to limited causal reasoning capability. Current methods attempt to leverage the powerful unde…
Action GenerationAutonomous DrivingBench2DriveLarge Language Model+4DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perceptio…
Autonomous DrivingBench2DriveConditional Image GenerationImage GenerationHydra-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 PredictionSimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment
Integrating large language models (LLMs) into autonomous driving has attracted significant attention with the hope of improving generalization and explainability. However, existing methods often focus on either driving o…
Autonomous DrivingBench2DriveLanguage ModelingLanguage Modelling+2HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder
Although end-to-end autonomous driving (E2E-AD) technologies have made significant progress in recent years, there remains an unsatisfactory performance on closed-loop evaluation. The potential of leveraging planning in …
Autonomous DrivingBench2DriveDecoderTrajectory PredictionDriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
End-to-end autonomous driving (E2E-AD) has emerged as a trend in the field of autonomous driving, promising a data-driven, scalable approach to system design. However, existing E2E-AD methods usually adopt the sequential…
Autonomous DrivingBench2DriveCAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
In this paper, we introduce CAPS (Context-Aware Priority Sampling), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses the challenge of imbalanced training dat…
Autonomous DrivingBench2DriveImitation Learning