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index.html

@@ -644,9 +644,9 @@ <h1>Bench2Drive</h1>
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 												Bench2Drive is the first benchmark for evaluating E2E-AD systems’ multiple abilities in a closed-loop manner. 
-												Bench2Drive consists of 2 million fully annotated frames as official training data, collected from 10000 short 
+												Bench2Drive consists of 2 million fully annotated frames as official training data, collected from 13638 short 
 												clips uniformly distributed under 44 interactive scenarios (cut-in, overtaking, detour, etc), 
-												23 weathers (sunny, foggy, rainy, etc), and 12 towns (urban, village, university, etc) in CARLA v2. 
+												23 weathers (sunny, foggy, rainy, etc), and 12 towns (urban, village, university, etc) in CARLA Leaderboard v2. 
 												Its evaluation protocol requires E2E-AD models to pass those 44 interactive scenarios under different 
 												locations which sums up to 220 routes and thus provide a comprehensive and disentangled assessment 
 												about their driving capability under different situations. We implement state-of-the-art E2E-AD models 
@@ -967,7 +967,7 @@ <h2>Benchmark</h2>
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-													src="./sources/benchmark.jpg"
+													src="./sources/benchmark_v2.PNG"
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@@ -1005,10 +1005,10 @@ <h2 class="title is-3">BibTeX
   journal={arXiv preprint arXiv:2406.03877},
   year={2024}
 }
-@article{li2024think,
+@inproceedings{li2024think,
   title={Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)},
   author={Qifeng Li and Xiaosong Jia and Shaobo Wang and Junchi Yan},
-  journal={arXiv preprint arXiv:2402.167200},
+  booktitle={ECCV},
   year={2024}
 }</code></pre>
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