Bench2Drive
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Benchmarks
Bench2Drive
Most implemented
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
iPad: Iterative Proposal-centric End-to-End Autonomous Driving
End-to-End Driving with Online Trajectory Evaluation via BEV World Model
Papers
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 Generalization