Update

app.py

@@ -2,26 +2,34 @@ import json
 import gradio as gr
 
 with open("results.json") as f:
-    data = json.load(f)
+    raw_data = json.load(f)
 
-data = sorted(data, key=lambda x: x["driving_score"], reverse=True)
+# Extract rows from the new format
+data = raw_data["datasets"][0]["sota"]["rows"]
+
+# Sort by driving score
+data = sorted(data, key=lambda x: float(x["metrics"]["Driving Score"]), reverse=True)
 
 rows_html = ""
 for idx, d in enumerate(data, 1):
-    paper = f'<a href="{d["paper_link"]}" target="_blank">📄 {d["paper_title"]}</a>' if d["paper_link"] else ""
+    # Extract paper info
+    paper_title = d.get("paper_title", "")
+    paper_url = d.get("paper_url", "")
+    paper = f'<a href="{paper_url}" target="_blank">📄 {paper_title}</a>' if paper_url and paper_title else ""
     
-    # Handle repository - check for null, "-", or empty string
-    repo_link = d.get("repository_link")
-    if repo_link and repo_link != "-" and repo_link != "":
-        repo = f'<a href="{repo_link}" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 16 16" fill="currentColor"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8z"></path></svg></a>'
+    # Extract repository info
+    code_links = d.get("code_links", [])
+    if code_links and len(code_links) > 0:
+        repo_url = code_links[0]["url"]
+        repo = f'<a href="{repo_url}" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 16 16" fill="currentColor"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8z"></path></svg></a>'
     else:
         repo = "-"
     
     row_class = "row-even" if idx % 2 == 0 else "row-odd"
     rows_html += f"""
     <tr class="{row_class}">
-        <td class="model-name">{d["name"]}</td>
-        <td class="score">{d["driving_score"]}</td>
+        <td class="model-name">{d["model_name"]}</td>
+        <td class="score">{d["metrics"]["Driving Score"]}</td>
         <td class="paper-cell">{paper}</td>
         <td class="repo-cell">{repo}</td>
     </tr>

results.json

@@ -1,45 +1,569 @@
-[
-  {
-    "name": "HiP-AD",
-    "driving_score": 86.77,
-    "paper_title": "HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder",
-    "paper_link": "https://arxiv.org/abs/2503.08612",
-    "repository": "nullmax-vision/HiP-AD",
-    "repository_link": "https://github.com/nullmax-vision/HiP-AD"
-  },
-  {
-    "name": "BridgeDrive",
-    "driving_score": 86.87,
-    "paper_title": "BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving",
-    "paper_link": "https://arxiv.org/abs/2509.23589",
-    "repository_link": null
-  },
-  {
-    "name": "SimLingo",
-    "driving_score": 85.94 ,
-    "paper_title": "SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment",
-    "paper_link": "https://arxiv.org/abs/2503.09594",
-    "repository_link": "https://github.com/RenzKa/simlingo"
-  },
-  {
-    "name": "TransFuser++",
-    "driving_score": 84.21 ,
-    "paper_title": "Hidden Biases of End-to-End Driving Datasets",
-    "paper_link": "https://arxiv.org/abs/2412.09602",
-    "repository_link": "https://github.com/autonomousvision/carla_garage"
-  },
-  {
-    "name": "AutoVLA",
-    "driving_score": 78.84,
-    "paper_title": "AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning",
-    "paper_link": "https://arxiv.org/abs/2506.13757",
-    "repository_link": "https://github.com/ucla-mobility/AutoVLA"
-  },
-  {
-    "name": "ORION",
-    "driving_score": 77.74 ,
-    "paper_title": "ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation",
-    "paper_link": "https://arxiv.org/abs/2503.19755",
-    "repository_link": "https://github.com/xiaomi-mlab/Orion"
-  }
-]
+{
+  "task": "Bench2Drive",
+  "categories": [],
+  "description": "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.",
+  "subtasks": [],
+  "synonyms": [],
+  "source_link": null,
+  "datasets": [
+    {
+      "sota": {
+        "rows": [
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/nullmax-vision/hip-ad",
+                "title": "nullmax-vision/hip-ad"
+              }
+            ],
+            "model_links": [],
+            "model_name": "HiP-AD",
+            "metrics": {
+              "Driving Score": "86.77"
+            },
+            "paper_date": "2025-03-11",
+            "paper_title": "HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2503.08612v1"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "R2SE",
+            "metrics": {
+              "Driving Score": "86.28"
+            },
+            "paper_date": null,
+            "paper_title": "",
+            "uses_additional_data": false,
+            "paper_url": ""
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/RenzKa/simlingo",
+                "title": "RenzKa/simlingo"
+              }
+            ],
+            "model_links": [],
+            "model_name": "SimLingo-Base (CarLLaVa)",
+            "metrics": {
+              "Driving Score": "85.94"
+            },
+            "paper_date": "2024-06-14",
+            "paper_title": "CarLLaVA: Vision language models for camera-only closed-loop driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2406.10165v1"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/autonomousvision/carla_garage",
+                "title": "autonomousvision/carla_garage"
+              }
+            ],
+            "model_links": [],
+            "model_name": "TransFuser++",
+            "metrics": {
+              "Driving Score": "84.21"
+            },
+            "paper_date": "2023-06-13",
+            "paper_title": "Hidden Biases of End-to-End Driving Models",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2306.07957v2"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "GaussianFusion",
+            "metrics": {
+              "Driving Score": "79.4"
+            },
+            "paper_date": "2025-05-27",
+            "paper_title": "GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2506.00034v1"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "ORION",
+            "metrics": {
+              "Driving Score": "77.7"
+            },
+            "paper_date": "2025-03-25",
+            "paper_title": "ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2503.19755v1"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "Raw2Drive",
+            "metrics": {
+              "Driving Score": "74.36"
+            },
+            "paper_date": "2025-05-22",
+            "paper_title": "Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2505.16394v1"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "ETA",
+            "metrics": {
+              "Driving Score": "74.33"
+            },
+            "paper_date": null,
+            "paper_title": "",
+            "uses_additional_data": false,
+            "paper_url": ""
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "DriveMoE",
+            "metrics": {
+              "Driving Score": "74.22"
+            },
+            "paper_date": "2025-05-22",
+            "paper_title": "DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2505.16278v1"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/woxihuanjiangguo/hydra-next",
+                "title": "woxihuanjiangguo/hydra-next"
+              }
+            ],
+            "model_links": [],
+            "model_name": "Hydra-NeXt",
+            "metrics": {
+              "Driving Score": "73.86"
+            },
+            "paper_date": "2025-03-15",
+            "paper_title": "Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2503.12030v1"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "VL (on failure)",
+            "metrics": {
+              "Driving Score": "73.29"
+            },
+            "paper_date": "2024-06-03",
+            "paper_title": "Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2406.01544v2"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "DRIVER",
+            "metrics": {
+              "Driving Score": "68.90"
+            },
+            "paper_date": null,
+            "paper_title": "",
+            "uses_additional_data": false,
+            "paper_url": ""
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "DiffAD",
+            "metrics": {
+              "Driving Score": "67.92"
+            },
+            "paper_date": "2025-03-15",
+            "paper_title": "DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2503.12170v1"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "NavigationDrive",
+            "metrics": {
+              "Driving Score": "67.17"
+            },
+            "paper_date": null,
+            "paper_title": "",
+            "uses_additional_data": false,
+            "paper_url": ""
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/Kguo-cs/iPad",
+                "title": "Kguo-cs/iPad"
+              }
+            ],
+            "model_links": [],
+            "model_name": "iPad",
+            "metrics": {
+              "Driving Score": "65.02"
+            },
+            "paper_date": "2025-05-21",
+            "paper_title": "iPad: Iterative Proposal-centric End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2505.15111v1"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/opendrivelab/driveadapter",
+                "title": "opendrivelab/driveadapter"
+              }
+            ],
+            "model_links": [],
+            "model_name": "DriveAdapter",
+            "metrics": {
+              "Driving Score": "64.22"
+            },
+            "paper_date": "2023-08-01",
+            "paper_title": "DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2308.00398v2"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/liuxueyi/reasonplan",
+                "title": "liuxueyi/reasonplan"
+              }
+            ],
+            "model_links": [],
+            "model_name": "ReasonPlan",
+            "metrics": {
+              "Driving Score": "64.01"
+            },
+            "paper_date": "2025-05-26",
+            "paper_title": "ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2505.20024v1"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/thinklab-sjtu/drivetransformer",
+                "title": "thinklab-sjtu/drivetransformer"
+              }
+            ],
+            "model_links": [],
+            "model_name": "Drivetransformer-Large",
+            "metrics": {
+              "Driving Score": "63.46"
+            },
+            "paper_date": "2025-03-07",
+            "paper_title": "DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2503.07656v1"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/opendrivelab/thinktwice",
+                "title": "opendrivelab/thinktwice"
+              }
+            ],
+            "model_links": [],
+            "model_name": "ThinkTwice",
+            "metrics": {
+              "Driving Score": "62.44"
+            },
+            "paper_date": "2023-05-10",
+            "paper_title": "Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2305.06242v1"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/OpenPerceptionX/TCP",
+                "title": "OpenPerceptionX/TCP"
+              }
+            ],
+            "model_links": [],
+            "model_name": "TCP-traj",
+            "metrics": {
+              "Driving Score": "59.90"
+            },
+            "paper_date": "2022-06-16",
+            "paper_title": "Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2206.08129v2"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/suhaisheng/difsd",
+                "title": "suhaisheng/difsd"
+              }
+            ],
+            "model_links": [],
+            "model_name": "DiFSD",
+            "metrics": {
+              "Driving Score": "52.02"
+            },
+            "paper_date": "2024-09-15",
+            "paper_title": "DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2409.09777v4"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "X-Driver",
+            "metrics": {
+              "Driving Score": "51.70"
+            },
+            "paper_date": "2025-05-08",
+            "paper_title": "X-Driver: Explainable Autonomous Driving with Vision-Language Models",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2505.05098v2"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/OpenPerceptionX/TCP",
+                "title": "OpenPerceptionX/TCP"
+              }
+            ],
+            "model_links": [],
+            "model_name": "TCP-traj w/o distillation",
+            "metrics": {
+              "Driving Score": "49.30"
+            },
+            "paper_date": "2022-06-16",
+            "paper_title": "Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2206.08129v2"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "CogAD",
+            "metrics": {
+              "Driving Score": "48.30"
+            },
+            "paper_date": "2025-05-27",
+            "paper_title": "CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2505.21581v2"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "MomAD",
+            "metrics": {
+              "Driving Score": "47.91"
+            },
+            "paper_date": null,
+            "paper_title": "",
+            "uses_additional_data": false,
+            "paper_url": ""
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/opendrivelab/uniad",
+                "title": "opendrivelab/uniad"
+              }
+            ],
+            "model_links": [],
+            "model_name": "UniAD-Base",
+            "metrics": {
+              "Driving Score": "45.81"
+            },
+            "paper_date": "2022-12-20",
+            "paper_title": "Planning-oriented Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2212.10156v2"
+          },
+          {
+            "code_links": [],
+            "model_links": [],
+            "model_name": "TTOG",
+            "metrics": {
+              "Driving Score": "45.23"
+            },
+            "paper_date": "2025-04-17",
+            "paper_title": "Two Tasks, One Goal: Uniting Motion and Planning for Excellent End To End Autonomous Driving Performance",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2504.12667v1"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/wzzheng/genad",
+                "title": "wzzheng/genad"
+              }
+            ],
+            "model_links": [],
+            "model_name": "GenAD",
+            "metrics": {
+              "Driving Score": "44.81"
+            },
+            "paper_date": "2024-02-18",
+            "paper_title": "GenAD: Generative End-to-End Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2402.11502v3"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/swc-17/sparsedrive",
+                "title": "swc-17/sparsedrive"
+              },
+              {
+                "url": "https://github.com/linxuewu/sparse4d",
+                "title": "linxuewu/sparse4d"
+              }
+            ],
+            "model_links": [],
+            "model_name": "SparseDrive",
+            "metrics": {
+              "Driving Score": "44.54"
+            },
+            "paper_date": "2024-05-30",
+            "paper_title": "SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2405.19620v2"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/hustvl/vad",
+                "title": "hustvl/vad"
+              },
+              {
+                "url": "https://github.com/yangyucheng000/papercode-2/tree/main/WS-VAD-mindspore-main",
+                "title": "yangyucheng000/papercode-2"
+              }
+            ],
+            "model_links": [],
+            "model_name": "VAD",
+            "metrics": {
+              "Driving Score": "42.35"
+            },
+            "paper_date": "2023-03-21",
+            "paper_title": "VAD: Vectorized Scene Representation for Efficient Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2303.12077v3"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/opendrivelab/uniad",
+                "title": "opendrivelab/uniad"
+              }
+            ],
+            "model_links": [],
+            "model_name": "UniAD-Tiny",
+            "metrics": {
+              "Driving Score": "40.73"
+            },
+            "paper_date": "2022-12-20",
+            "paper_title": "Planning-oriented Autonomous Driving",
+            "uses_additional_data": false,
+            "paper_url": "https://arxiv.org/abs/2212.10156v2"
+          },
+          {
+            "code_links": [
+              {
+                "url": "https://github.com/OpenPerceptionX/TCP",
+                "title": "OpenPerceptionX/TCP"
+              }
+            ],
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+            "paper_url": "https://arxiv.org/abs/2206.08129v2"
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+            },
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+            "paper_title": "From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving",
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+            "paper_url": "https://arxiv.org/abs/2505.22067v1"
+          },
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+                "title": "OpenPerceptionX/TCP"
+              }
+            ],
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+                "title": "Thinklab-SJTU/Bench2Drive"
+              },
+              {
+                "url": "https://github.com/autonomousvision/carla_garage",
+                "title": "autonomousvision/carla_garage"
+              },
+              {
+                "url": "https://github.com/thinklab-sjtu/bench2drivezoo",
+                "title": "thinklab-sjtu/bench2drivezoo"
+              },
+              {
+                "url": "https://github.com/RenzKa/simlingo",
+                "title": "RenzKa/simlingo"
+              }
+            ],
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+            "paper_url": "https://arxiv.org/abs/2406.03877v3"
+          }
+        ],
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+          "title": "Papers with Code Leaderboard URL"
+        }
+      ],
+      "description": "",
+      "subdatasets": [],
+      "dataset_citations": [],
+      "dataset": "Bench2Drive"
+    }
+  ]
+}