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Bench2Drive

Bench2Drive is an autonomous driving benchmark based on the CARLA leaderboard 2.0. It consists of 220 short routes featuring safety critical scenarios. The evaluation is performed closed-loop in the CARLA simulator. The performance of an entire driving stack is being evaluated.

Papers

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Showing 26–35 of 35 papers

TitleStatusHype
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving—0
Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving—0
X-Driver: Explainable Autonomous Driving with Vision-Language Models—0
Two Tasks, One Goal: Uniting Motion and Planning for Excellent End To End Autonomous Driving Performance—0
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation—0
DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving—0
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving—0
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning—0
Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model—0
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning—0
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