Telkwevr/Bench2Drive-VL-base
Bench2Drive-VL: Full-Stack Software for Closed-Loop Autonomous Driving with Vision Language Models Project Page | GitHub | Paper Bench2Drive-VL is a comprehensive closed-loop benchmark for Vision-Language Models in Autonomous Driving (VLM4AD). It extends the Bench2Drive benchmark by introducing closed-loop evaluation and the DriveCommenter expert model for automated annotation. This repository contains the natural language annotations for the Bench2Drive-Base1000 dataset. These… See the full description on the dataset page: https://huggingface.co/datasets/Telkwevr/Bench2Drive-VL-base.
19.9k
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important objects in the scene are the navy car to the front of the ego vehicle and the pedestrian crossing the road to the front of the ego vehicle(<c7537<CAM_FRONT,637.0,605.5>>).",1187 "C": null,1188 "qid": 18,1189 "con_up": -1,1190 "con_down": -1,1191 "cluster": 0,1192 "layer": 0,1193 "object_tags": [1194 "<c7573<CAM_FRONT,804.8,475.4>>",1195 "<c7537<CAM_FRONT,637.0,605.5>>"1196 ]1197 },1198 {1199 "Q": "What are the important objects in the scene? List them from most to least important.",1200 "A": "The pedestrian crossing the road to the front of the ego vehicle(<c7537<CAM_FRONT,637.0,605.5>>), the navy car that is to the front of the ego vehicle(<c7573<CAM_FRONT,804.8,475.4>>).",