{"id":26614,"date":"2025-03-21T17:21:31","date_gmt":"2025-03-21T09:21:31","guid":{"rendered":"http:\/\/192.168.10.115\/?p=26614"},"modified":"2025-03-21T17:21:31","modified_gmt":"2025-03-21T09:21:31","slug":"2025-03-21-%e9%a2%84%e6%b5%8b%e8%af%af%e5%b7%ae%e9%99%8d%e4%bd%8e12-3%ef%bc%8c%e5%a4%9a%e8%bd%a6%e5%8d%8f%e5%90%8c%e9%a2%84%e6%b5%8b%e6%a1%86%e6%9e%b6cmp%ef%bc%8c%e7%a0%b4%e8%a7%a3%e8%87%aa%e5%8a%a8","status":"publish","type":"post","link":"http:\/\/222.128.65.89:18086\/index.php\/2025\/03\/21\/26614\/","title":{"rendered":"2025-03-21 \u9884\u6d4b\u8bef\u5dee\u964d\u4f4e12.3%\uff0c\u591a\u8f66\u534f\u540c\u9884\u6d4b\u6846\u67b6CMP\uff0c\u7834\u89e3\u81ea\u52a8\u9a7e\u9a76\u89c6\u7ebf\u76f2\u533a"},"content":{"rendered":"\n

\u539f\u6587\u94fe\u63a5\uff1ahttps:\/\/www.163.com\/dy\/article\/JR5T95OF0511AQHO.html<\/a><\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n

2025 \u5e74 3 \u6708\uff0c\u52a0\u5dde\u5927\u5b66\u6cb3\u6ee8\u5206\u6821\u4e0e\u5bc6\u6b47\u6839\u5927\u5b66\u3001\u52a0\u5dde\u5927\u5b66\u4f2f\u514b\u5229\u5206\u6821\u4ee5\u53ca\u534e\u76db\u987f\u5927\u5b66\u8054\u5408\u56e2\u961f\u5728\u673a\u5668\u4eba\u9886\u57df\u9876\u7ea7\u671f\u520a\u300aIEEE Robotics and Automation Letters\u300b\u53d1\u8868\u6700\u65b0\u7814\u7a76\u6210\u679c \u2014\u2014CMP\uff08Cooperative Motion Prediction\uff09<\/strong>\uff0c\u9996\u6b21\u63d0\u51fa\u4e00\u79cd\u9762\u5411\u8f66\u8054\u7f51\uff08V2X\uff09\u7684\u534f\u540c\u8fd0\u52a8\u9884\u6d4b\u6846\u67b6\uff0c\u901a\u8fc7\u591a\u8f66\u4fe1\u606f\u5171\u4eab\u4e0e\u878d\u5408\uff0c\u663e\u8457\u63d0\u5347\u81ea\u52a8\u9a7e\u9a76\u8f66\u8f86\u7684\u8f68\u8ff9\u9884\u6d4b\u7cbe\u5ea6\u4e0e\u573a\u666f\u9002\u5e94\u80fd\u529b\u3002\u8be5\u6280\u672f\u5df2\u5728\u771f\u5b9e\u573a\u666f\u6570\u636e\u96c6 V2V4Real \u548c\u4eff\u771f\u5e73\u53f0 OPV2V \u4e2d\u9a8c\u8bc1\u5176\u9ad8\u6548\u6027\uff0c\u76f8\u6bd4\u73b0\u6709\u6700\u4f18\u6a21\u578b\uff0c\u9884\u6d4b\u8bef\u5dee\u964d\u4f4e 12.3%\uff0c\u4e3a\u590d\u6742\u4ea4\u901a\u73af\u5883\u4e0b\u7684\u81ea\u52a8\u9a7e\u9a76\u5b89\u5168\u51b3\u7b56\u63d0\u4f9b\u4e86\u5168\u65b0\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n
    \n
  • \u8bba\u6587\u6807\u9898\uff1aCMP: Cooperative Motion Prediction with Multi-Agent Communication<\/li>\n\n\n\n
  • \u8bba\u6587\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2403.17916<\/li>\n\n\n\n
  • \u9879\u76ee\u7f51\u7ad9\uff1ahttps:\/\/cmp-cooperative-prediction.github.io<\/li>\n\n\n\n
  • \u4ee3\u7801\u5f00\u6e90\uff1ahttps:\/\/github.com\/tasl-lab\/CMP<\/li>\n<\/ul>\n\n\n\n

    \u6280\u672f\u4eae\u70b9\uff1a\u611f\u77e5 – \u9884\u6d4b\u4e00\u4f53\u5316\u534f\u540c\uff0c\u7834\u89e3\u81ea\u52a8\u9a7e\u9a76 \u201c\u89c6\u7ebf\u76f2\u533a\u201d<\/strong><\/p>\n\n\n\n

    \u4f20\u7edf\u81ea\u52a8\u9a7e\u9a76\u7cfb\u7edf\u4f9d\u8d56\u5355\u8f66\u4f20\u611f\u5668\uff0c\u6613\u53d7\u906e\u6321\u6216\u6781\u7aef\u5929\u6c14\u5f71\u54cd\uff0c\u5bfc\u81f4\u611f\u77e5\u4e0e\u9884\u6d4b\u80fd\u529b\u53d7\u9650\u3002CMP \u901a\u8fc7\u591a\u8f66\u534f\u540c\u611f\u77e5\u4e0e\u9884\u6d4b\u7684\u6df1\u5ea6\u878d\u5408<\/strong>\uff0c\u6253\u7834\u5355\u8f66\u4fe1\u606f\u5b64\u5c9b\uff0c\u5b9e\u73b0 \u201c\u5168\u5c40\u89c6\u91ce\u201d \u4e0e \u201c\u52a8\u6001\u63a8\u7406\u201d \u7684\u53cc\u91cd\u7a81\u7834\uff1a<\/p>\n\n\n\n

    1. \u9ad8\u6548\u534f\u540c\u611f\u77e5<\/strong><\/p>\n\n\n\n

      \n
    • LiDAR \u6570\u636e\u5171\u4eab\u4e0e\u538b\u7f29\uff1a\u5229\u7528 256 \u500d\u538b\u7f29\u7387\u7684\u9e1f\u77b0\u56fe\uff08BEV\uff09\u7279\u5f81\u4f20\u8f93\uff0c\u5e26\u5bbd\u9700\u6c42\u4ece 82.5 MB\/s \u964d\u81f3 0.32 MB\/s\uff0c\u517c\u987e\u901a\u4fe1\u6548\u7387\u4e0e\u611f\u77e5\u7cbe\u5ea6\u3002<\/li>\n\n\n\n
    • \u5ef6\u8fdf\u9c81\u68d2\u6027\uff1a\u652f\u6301 100ms \u5185\u901a\u4fe1\u5ef6\u8fdf\uff0c\u901a\u8fc7\u65f6\u7a7a\u5bf9\u9f50\u4e0e\u591a\u5e27\u540c\u6b65\u5904\u7406\uff0c\u786e\u4fdd\u6570\u636e\u5728\u52a8\u6001\u573a\u666f\u4e0b\u7684\u6709\u6548\u6027\u3002<\/li>\n<\/ul>\n\n\n\n

      2. \u52a8\u6001\u8f68\u8ff9\u9884\u6d4b<\/strong><\/p>\n\n\n\n

        \n
      • \u591a\u6a21\u6001\u9884\u6d4b\u89e3\u7801\u5668\uff1a\u57fa\u4e8e Transformer \u67b6\u6784\uff0c\u7ed3\u5408\u9ad8\u65af\u6df7\u5408\u6a21\u578b\uff08GMM\uff09\uff0c\u751f\u6210\u591a\u6837\u5316\u7684\u672a\u6765\u8f68\u8ff9\u5047\u8bbe\uff0c\u8986\u76d6\u8f66\u8f86\u8f6c\u5411\u3001\u907f\u8ba9\u7b49\u591a\u79cd\u884c\u4e3a\u6a21\u5f0f\u3002<\/li>\n\n\n\n
      • \u5b9e\u65f6\u9884\u6d4b\u805a\u5408\uff1a\u901a\u8fc7\u6ce8\u610f\u529b\u673a\u5236\u52a8\u6001\u6574\u5408\u591a\u8f66\u9884\u6d4b\u7ed3\u679c\uff0c\u4f18\u5148\u91c7\u7eb3\u90bb\u8fd1\u8f66\u8f86\u7684\u53ef\u9760\u9884\u6d4b\uff0c\u8fdb\u4e00\u6b65\u63d0\u5347\u957f\u65f6\u9884\u6d4b\uff085 \u79d2\uff09\u7684\u51c6\u786e\u6027\u3002<\/li>\n<\/ul>\n\n\n\n

        \u4e00\uff0e\u7814\u7a76\u80cc\u666f<\/strong><\/p>\n\n\n\n

        \u81ea\u52a8\u9a7e\u9a76\u6280\u672f\u7684\u6838\u5fc3\u5728\u4e8e\u5bf9\u73af\u5883\u7684\u7cbe\u51c6\u611f\u77e5\u4e0e\u672a\u6765\u8f68\u8ff9\u7684\u53ef\u9760\u9884\u6d4b\u3002\u7136\u800c\uff0c\u4f20\u7edf\u7cfb\u7edf\u4f9d\u8d56\u5355\u8f66\u4f20\u611f\u5668\uff08\u5982 LiDAR\u3001\u6444\u50cf\u5934\uff09\uff0c\u5b58\u5728\u89c6\u91ce\u53d7\u9650\u3001\u6613\u53d7\u906e\u6321\u7684\u56fa\u6709\u7f3a\u9677 \u2014\u2014 \u4f8b\u5982\uff0c\u57ce\u5e02\u8def\u53e3\u88ab\u5efa\u7b51\u7269\u906e\u6321\u7684\u8f66\u8f86\u3001\u9ad8\u901f\u573a\u666f\u4e2d\u7684\u7a81\u7136\u5207\u5165\u76ee\u6807\uff0c\u4ec5\u51ed\u5355\u8f66\u611f\u77e5\u6781\u6613\u51fa\u73b0\u6f0f\u68c0\u6216\u8bef\u5224\uff0c\u5bfc\u81f4\u51b3\u7b56\u5ef6\u8fdf\u751a\u81f3\u5b89\u5168\u4e8b\u6545\u3002\u4e3a\u7a81\u7834\u8fd9\u4e00\u9650\u5236\uff0c\u534f\u540c\u611f\u77e5\uff08Cooperative Perception\uff09\u5e94\u8fd0\u800c\u751f\u3002\u901a\u8fc7\u8f66\u8054\u7f51\uff08V2X\uff09\u6280\u672f\uff0c\u8f66\u8f86\u53ef\u5171\u4eab\u4f20\u611f\u5668\u6570\u636e\uff0c\u6784\u5efa\u5168\u5c40\u73af\u5883\u8ba4\u77e5\u3002\u73b0\u6709\u7814\u7a76\uff08\u5982 V2VNet\u3001CoBEVT\uff09\u5df2\u8bc1\u660e\uff0c\u591a\u8f66\u534f\u540c\u80fd\u663e\u8457\u63d0\u5347\u76ee\u6807\u68c0\u6d4b\u7cbe\u5ea6\uff0c\u4f46\u8fd9\u7c7b\u5de5\u4f5c\u591a\u5c40\u9650\u4e8e\u611f\u77e5\u5c42\u7684\u4fe1\u606f\u878d\u5408\uff0c\u672a\u6df1\u5165\u6316\u6398\u534f\u540c\u6570\u636e\u5bf9\u8fd0\u52a8\u9884\u6d4b\u7684\u4ef7\u503c\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u5355\u8f66\u8f68\u8ff9\u9884\u6d4b\u6a21\u578b\u5982 MTR\uff0c\u5e76\u672a\u8003\u8651\u591a\u8f66\u4ea4\u4e92\uff0c\u534f\u540c\u9884\u6d4b\u3002\u5728\u6b64\u80cc\u666f\u4e0b\uff0cCMP\uff08Cooperative Motion Prediction\uff09\u5e94\u52bf\u800c\u751f\u3002\u4f5c\u4e3a\u9996\u4e2a\u611f\u77e5 – \u9884\u6d4b\u4e00\u4f53\u5316\u534f\u540c\u6846\u67b6\uff0cCMP \u4e0d\u4ec5\u901a\u8fc7\u9ad8\u6548 BEV \u7279\u5f81\u5171\u4eab\u4e0e\u538b\u7f29\u6280\u672f\u6269\u5c55\u611f\u77e5\u8fb9\u754c\uff0c\u66f4\u521b\u65b0\u6027\u5730\u5f15\u5165\u9884\u6d4b\u805a\u5408\u6a21\u5757\uff0c\u52a8\u6001\u878d\u5408\u591a\u8f66\u9884\u6d4b\u7ed3\u679c\uff0c\u5728\u771f\u5b9e\u901a\u4fe1\u7ea6\u675f\u4e0b\u5b9e\u73b0 \u201c\u5168\u5c40\u611f\u77e5 – \u7cbe\u51c6\u9884\u6d4b – \u534f\u540c\u51b3\u7b56\u201d \u7684\u95ed\u73af\uff0c\u4e3a\u590d\u6742\u52a8\u6001\u573a\u666f\u4e2d\u7684\u81ea\u52a8\u9a7e\u9a76\u5b89\u5168\u6811\u7acb\u65b0\u6807\u6746\u3002<\/p>\n\n\n\n

        \u4e8c\uff0e\u7814\u7a76\u65b9\u6cd5\uff1a\u611f\u77e5 – \u9884\u6d4b – \u805a\u5408\u4e09\u9636\u534f\u540c\u6846\u67b6<\/strong><\/p>\n\n\n\n

        \"\"\/<\/figure>\n\n\n\n

        CMP \u4ee5\u591a\u8f66\u534f\u540c\u611f\u77e5\u4e3a\u57fa\u7840\u3001\u52a8\u6001\u8f68\u8ff9\u9884\u6d4b\u4e3a\u6838\u5fc3\u3001\u9884\u6d4b\u805a\u5408\u4f18\u5316\u4e3a\u95ed\u73af<\/strong>\uff0c\u6784\u5efa\u4e86\u4e00\u5957\u5b8c\u6574\u7684\u4e09\u9636\u534f\u540c\u6846\u67b6\uff0c\u5176\u6280\u672f\u8def\u5f84\u5982\u4e0b\uff1a<\/p>\n\n\n\n

        1. \u534f\u540c\u611f\u77e5\uff1a\u591a\u8f66 LiDAR \u6570\u636e\u7684\u9ad8\u6548\u878d\u5408<\/strong><\/p>\n\n\n\n

        \u76ee\u6807\uff1a\u7a81\u7834\u5355\u8f66\u611f\u77e5\u76f2\u533a\uff0c\u6784\u5efa\u5168\u5c40\u73af\u5883\u8868\u5f81\u3002<\/p>\n\n\n\n

          \n
        • BEV \u7279\u5f81\u63d0\u53d6\u4e0e\u538b\u7f29\uff1a<\/li>\n<\/ul>\n\n\n\n

          \u91c7\u7528\u6539\u8fdb\u7248 CoBEVT \u4f5c\u4e3a\u9aa8\u5e72\u7f51\u7edc\uff0c\u5c06\u6bcf\u8f86\u8f66\u7684 LiDAR \u70b9\u4e91\u8f6c\u6362\u4e3a\u9e1f\u77b0\u56fe\uff08BEV\uff09\u7279\u5f81\uff08\u5206\u8fa8\u7387 0.4m\u00d70.4m\uff09\uff0c\u901a\u8fc7\u5377\u79ef\u81ea\u7f16\u7801\u5668\u8fdb\u884c 256 \u500d\u538b\u7f29\uff0c\u5e26\u5bbd\u9700\u6c42\u4ece 82.5 MB\/s \u964d\u81f3 0.32 MB\/s\uff0c\u6ee1\u8db3\u8f66\u89c4\u7ea7\u901a\u4fe1\u8981\u6c42\u3002<\/p>\n\n\n\n

            \n
          • \u65f6\u7a7a\u5bf9\u9f50\u4e0e\u5ef6\u8fdf\u8865\u507f\uff1a<\/li>\n<\/ul>\n\n\n\n

            \u57fa\u4e8e GPS \u540c\u6b65\u65f6\u949f\uff0c\u8bbe\u8ba1 100ms \u901a\u4fe1\u7a97\u53e3\uff0c\u901a\u8fc7\u53ef\u5fae\u5206\u7a7a\u95f4\u53d8\u6362\u7b97\u5b50\uff08STO\uff09\u5bf9\u9f50\u591a\u8f66\u5750\u6807\u7cfb\uff0c\u52a8\u6001\u4e22\u5f03\u8d85\u65f6\u6570\u636e\uff0c\u89e3\u51b3\u8f66\u8f86\u8fd0\u52a8\u4e0e\u901a\u4fe1\u5ef6\u8fdf\u5bfc\u81f4\u7684\u7279\u5f81\u9519\u4f4d\u95ee\u9898\u3002<\/p>\n\n\n\n

              \n
            • \u8de8\u8f66\u7279\u5f81\u878d\u5408\uff1a<\/li>\n<\/ul>\n\n\n\n

              \u5229\u7528 FuseBEVT \u6a21\u5757\u805a\u5408\u591a\u8f66 BEV \u7279\u5f81\uff0c\u7ed3\u5408\u8f7b\u91cf\u5316\u68c0\u6d4b\u5934\u8f93\u51fa 3D \u76ee\u6807\u6846\uff0c\u68c0\u6d4b\u7cbe\u5ea6\uff08AP@0.7\uff09\u8fbe 0.82\uff0c\u8f83\u5355\u8f66\u95f4\u6b65\u957f\u63d0\u5347 24%\u3002<\/p>\n\n\n\n

                \n
              • \u591a\u7269\u4f53\u8ddf\u8e2a\uff1a<\/li>\n<\/ul>\n\n\n\n

                \u5bf9\u4e8e\u68c0\u6d4b\u51fa\u7684\u7269\u4f53\uff0c\u573a\u666f\u4e2d\u6bcf\u4e00\u53f0 CAV \u72ec\u81ea\u4f7f\u7528\u57fa\u4e8e AB3DMOT \u7684\u8ddf\u8e2a\u7b97\u6cd5\u8fdb\u884c\u8ddf\u8e2a\u3002<\/p>\n\n\n\n

                2. \u8f68\u8ff9\u9884\u6d4b\uff1a\u591a\u6a21\u6001 Transformer \u89e3\u7801\u5668<\/strong><\/p>\n\n\n\n

                \u76ee\u6807\uff1a\u57fa\u4e8e\u5386\u53f2\u8f68\u8ff9\u4e0e\u573a\u666f\u4e0a\u4e0b\u6587\uff0c\u751f\u6210\u591a\u6837\u5316\u7684\u672a\u6765\u8f68\u8ff9\u5047\u8bbe\u3002<\/p>\n\n\n\n

                  \n
                • \u573a\u666f\u7f16\u7801\u4e0e\u610f\u56fe\u5efa\u6a21\uff1a<\/li>\n<\/ul>\n\n\n\n

                  \u5f15\u5165 MTR \u6a21\u578b\u6846\u67b6\uff0c\u901a\u8fc7 Polyline \u7f16\u7801\u5668\u63d0\u53d6\u8f66\u8f86\u8f68\u8ff9\u7279\u5f81\uff0cViT \u7f16\u7801\u5668\u63d0\u53d6\u9ad8\u7cbe\u5730\u56fe\u8bed\u4e49\uff0c\u7ed3\u5408 Transformer \u878d\u5408\u5c40\u90e8\u4ea4\u4e92\u4e0e\u5168\u5c40\u610f\u56fe\u3002<\/p>\n\n\n\n

                    \n
                  • \u52a8\u6001\u610f\u56fe\u70b9\u805a\u7c7b\uff1a<\/li>\n<\/ul>\n\n\n\n

                    \u91c7\u7528 k-means \u5bf9\u5386\u53f2\u8f68\u8ff9\u7ec8\u70b9\u805a\u7c7b\uff0c\u751f\u6210 64 \u4e2a\u610f\u56fe\u70b9\uff08Intention Points\uff09\uff0c\u8868\u5f81\u8f6c\u5411\u3001\u76f4\u884c\u3001\u505c\u8f66\u7b49\u591a\u6a21\u6001\u76ee\u6807\u3002<\/p>\n\n\n\n

                      \n
                    • \u9ad8\u65af\u6df7\u5408\u8f68\u8ff9\u751f\u6210\uff1a<\/li>\n<\/ul>\n\n\n\n

                      \u901a\u8fc7 Transformer \u89e3\u7801\u5668\u8fed\u4ee3\u4f18\u5316\u8f68\u8ff9\uff0c\u8f93\u51fa\u9ad8\u65af\u6df7\u5408\u6a21\u578b\uff08GMM\uff09\u53c2\u6570\uff0c\u8986\u76d6\u672a\u6765 5 \u79d2\u5185\u4f4d\u7f6e\u5206\u5e03\uff08\u5747\u503c \u03bc\u3001\u65b9\u5dee \u03c3\u3001\u76f8\u5173\u6027 \u03c1\uff09\uff0c\u652f\u6301\u6982\u7387\u5316\u591a\u6a21\u6001\u9884\u6d4b\u3002<\/p>\n\n\n\n

                      3. \u9884\u6d4b\u805a\u5408\uff1a\u6ce8\u610f\u529b\u9a71\u52a8\u7684\u534f\u540c\u4f18\u5316<\/strong><\/p>\n\n\n\n

                      \u76ee\u6807\uff1a\u6574\u5408\u591a\u8f66\u9884\u6d4b\u7ed3\u679c\uff0c\u6291\u5236\u5355\u89c6\u89d2\u8bef\u5dee\uff0c\u63d0\u5347\u5168\u5c40\u4e00\u81f4\u6027\u3002<\/p>\n\n\n\n

                        \n
                      • \u8de8\u8f66\u9884\u6d4b\u5bf9\u9f50\uff1a<\/li>\n<\/ul>\n\n\n\n

                        \u5c06\u5404\u8f66\u7684 GMM \u53c2\u6570\u3001\u5c40\u90e8\u5730\u56fe\u4e0e BEV \u7279\u5f81\u62fc\u63a5\u4e3a\u7edf\u4e00\u8f93\u5165\uff0c\u901a\u8fc7 MLP \u7f16\u7801\u4e3a\u8054\u5408\u7279\u5f81\u5411\u91cf\u3002<\/p>\n\n\n\n

                          \n
                        • \u591a\u5c42\u6ce8\u610f\u529b\u878d\u5408\uff1a<\/li>\n<\/ul>\n\n\n\n

                          \u8bbe\u8ba1 8 \u5934 – 5 \u5c42 Transformer \u67b6\u6784\uff0c\u52a8\u6001\u52a0\u6743\u591a\u8f66\u9884\u6d4b\u7f6e\u4fe1\u5ea6\uff08\u5982\u90bb\u8fd1\u8f66\u8f86\u9884\u6d4b\u6743\u91cd\u66f4\u9ad8\uff09\uff0c\u81ea\u9002\u5e94\u8865\u507f\u901a\u4fe1\u4e22\u5305\u6216\u906e\u6321\u5bfc\u81f4\u7684\u9884\u6d4b\u7f3a\u5931\u3002<\/p>\n\n\n\n

                            \n
                          • \u7aef\u5230\u7aef\u8054\u5408\u8bad\u7ec3\uff1a<\/li>\n<\/ul>\n\n\n\n

                            \u91c7\u7528\u591a\u4efb\u52a1\u635f\u5931\u51fd\u6570\uff0c\u540c\u6b65\u4f18\u5316\u68c0\u6d4b\uff08Focal Loss + L1 Loss\uff09\u3001\u9884\u6d4b\uff08NLL + L2 Loss\uff09\u4e0e\u805a\u5408\u6a21\u5757\uff0c\u907f\u514d\u8bef\u5dee\u8de8\u9636\u6bb5\u7d2f\u79ef\u3002<\/p>\n\n\n\n

                            \u6280\u672f\u5bf9\u6bd4\uff1aCMP \u7684\u521b\u65b0\u7a81\u7834<\/p>\n\n\n\n

                            \"\"\/<\/figure>\n\n\n\n

                            CMP \u901a\u8fc7\u611f\u77e5 – \u9884\u6d4b\u8054\u5408\u5efa\u6a21\u4e0e\u8f7b\u91cf\u5316\u901a\u4fe1\u8bbe\u8ba1\uff0c<\/strong>\u9996\u6b21\u5728\u771f\u5b9e\u8f66\u8054\u7f51\u7ea6\u675f\u4e0b\u5b9e\u73b0\u591a\u8f66\u534f\u540c\u8fd0\u52a8\u9884\u6d4b\uff0c\u4e3a\u590d\u6742\u52a8\u6001\u573a\u666f\u7684\u81ea\u52a8\u9a7e\u9a76\u51b3\u7b56\u63d0\u4f9b\u4e86\u53ef\u9760\u6280\u672f\u5e95\u5ea7\u3002<\/p>\n\n\n\n

                            \u4e09\uff0e\u5b9e\u9a8c\u7ed3\u679c\u548c\u5206\u6790\uff1a\u534f\u540c\u9a71\u52a8\u9884\u6d4b\u7cbe\u5ea6\u5168\u9762\u7a81\u7834<\/strong><\/p>\n\n\n\n

                            CMP \u5728 OPV2V\uff08\u4eff\u771f\uff09\u4e0e V2V4Real\uff08\u771f\u5b9e\u573a\u666f\uff09\u4e24\u5927\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u4e86\u7cfb\u7edf\u6027\u9a8c\u8bc1\uff0c\u6db5\u76d6\u611f\u77e5\u3001\u8ddf\u8e2a\u3001\u9884\u6d4b\u5168\u94fe\u8def\u6027\u80fd\u8bc4\u4f30\uff0c\u5e76\u4e0e V2VNet\u3001CoBEVT \u7b49\u524d\u6cbf\u6a21\u578b\u5bf9\u6bd4\uff0c\u6838\u5fc3\u7ed3\u8bba\u5982\u4e0b\uff1a<\/p>\n\n\n\n

                            1. \u8fd0\u52a8\u9884\u6d4b\uff1a\u957f\u65f6\u8bef\u5dee\u964d\u4f4e 19%<\/strong><\/p>\n\n\n\n

                            \u5728 5 \u79d2\u957f\u65f6\u9884\u6d4b\u4efb\u52a1\u4e2d\uff0cCMP \u51ed\u501f\u534f\u540c\u611f\u77e5 – \u9884\u6d4b\u8054\u5408\u4f18\u5316\uff0c\u663e\u8457\u4f18\u4e8e\u73b0\u6709\u65b9\u6848 V2VNet\uff1a<\/p>\n\n\n\n

                            \"\"\/<\/figure>\n\n\n\n
                            \"\"\/<\/figure>\n\n\n\n
                            \"\"\/<\/figure>\n\n\n\n

                            \u5173\u952e\u53d1\u73b0\uff1a<\/p>\n\n\n\n

                              \n
                            • \u534f\u540c\u611f\u77e5\u8d21\u732e\u663e\u8457\uff1a\u4ec5\u542f\u7528\u534f\u540c\u611f\u77e5\uff08\u4e0d\u53e0\u52a0\u9884\u6d4b\u805a\u5408\uff09\uff0c\u9884\u6d4b\u8bef\u5dee\u8f83\u65e0\u534f\u540c\u964d\u4f4e 10%-15%\uff0c\u8bc1\u660e\u591a\u8f66\u6570\u636e\u878d\u5408\u53ef\u6709\u6548\u6269\u5c55\u611f\u77e5\u8fb9\u754c\u3002<\/li>\n\n\n\n
                            • \u9884\u6d4b\u805a\u5408\u589e\u76ca\u7a81\u51fa\uff1a\u5f15\u5165\u8de8\u8f66\u9884\u6d4b\u805a\u5408\u6a21\u5757\u540e\uff0c\u8bef\u5dee\u8fdb\u4e00\u6b65\u964d\u4f4e 5%-8%\uff0c\u51f8\u663e\u591a\u89c6\u89d2\u9884\u6d4b\u4e92\u8865\u7684\u4ef7\u503c\u3002<\/li>\n\n\n\n
                            • \u771f\u5b9e\u573a\u666f\u4f18\u52bf\u66f4\u5927\uff1aV2V4Real \u4e2d CMP \u7684 minFDE\u2086\u76f8\u5bf9\u63d0\u5347\u8fbe 19%\uff0c\u8868\u660e\u5176\u5bf9\u906e\u6321\u3001\u590d\u6742\u4ea4\u4e92\u7684\u5f3a\u9002\u5e94\u80fd\u529b\u3002<\/li>\n\n\n\n
                            • \u5927\u8303\u56f4\u573a\u666f\u6548\u679c\u66f4\u660e\u663e\uff1a\u8fdb\u4e00\u6b65\u6839\u636e CAV \u5728\u573a\u666f\u4e2d\u7684\u6563\u5e03\u663e\u793a\uff0c\u968f\u7740\u534f\u540c\u8f66\u8f86\u8986\u76d6\u533a\u57df\u6269\u5927\uff08>200 m\u00b2\uff09\uff0cCMP \u7684\u9884\u6d4b\u7cbe\u5ea6\u63d0\u5347\u9ad8\u8fbe 28.4%\uff0c\u5145\u5206\u9a8c\u8bc1\u5176\u5728\u5927\u8303\u56f4\u590d\u6742\u573a\u666f\u4e2d\u7684\u4f18\u52bf\u3002<\/li>\n<\/ul>\n\n\n\n

                              2. \u611f\u77e5\u4e0e\u8ddf\u8e2a\uff1a\u9ad8\u538b\u7f29\u7387\u4e0b\u7684\u7cbe\u5ea6\u4fdd\u6301<\/strong><\/p>\n\n\n\n

                              CMP \u5728\u4fdd\u8bc1\u901a\u4fe1\u6548\u7387\u7684\u540c\u65f6\uff0c\u7ef4\u6301\u4e86\u9ad8\u7cbe\u5ea6\u611f\u77e5\u4e0e\u7a33\u5b9a\u8ddf\u8e2a\uff1a<\/p>\n\n\n\n

                              \"\"\/<\/figure>\n\n\n\n

                              \u5173\u952e\u53d1\u73b0\uff1a<\/p>\n\n\n\n

                                \n
                              • \u9ad8\u6548\u538b\u7f29\u53ef\u884c\u6027\uff1a256 \u500d BEV \u7279\u5f81\u538b\u7f29\u51e0\u4e4e\u4e0d\u4f1a\u5bfc\u81f4\u68c0\u6d4b\u7cbe\u5ea6\u4e0b\u964d\uff0c\u9a8c\u8bc1\u8f7b\u91cf\u5316\u901a\u4fe1\u8bbe\u8ba1\u7684\u6709\u6548\u6027\u3002<\/li>\n\n\n\n
                              • \u8ddf\u8e2a\u9c81\u68d2\u6027\u63d0\u5347\uff1a\u591a\u8f66\u534f\u540c\u51cf\u5c11\u6f0f\u68c0\u4e0e ID \u5207\u6362\uff0cMOTA \u63d0\u5347 15%-25%\uff0c\u4e3a\u957f\u65f6\u9884\u6d4b\u63d0\u4f9b\u66f4\u5b8c\u6574\u7684\u5386\u53f2\u8f68\u8ff9\u8f93\u5165\u3002<\/li>\n<\/ul>\n\n\n\n

                                3. \u901a\u4fe1\u6548\u7387\u4e0e\u5ef6\u8fdf\u9c81\u68d2\u6027<\/strong><\/p>\n\n\n\n

                                \"\"\/<\/figure>\n\n\n\n

                                CMP \u5728\u771f\u5b9e\u901a\u4fe1\u7ea6\u675f\u4e0b\u4ecd\u4fdd\u6301\u7a33\u5b9a\u6027\u80fd\uff1a<\/p>\n\n\n\n

                                  \n
                                • \u8ba1\u7b97\u901f\u5ea6\uff1aCMP \u4e2d\u6240\u6709\u8ba1\u7b97\u90e8\u5206\u53ef\u4ee5\u5728 100ms \u5b8c\u6210\uff0c\u7b26\u5408\u4e1a\u754c\u65e0\u4eba\u9a7e\u9a76\u9700\u6c42\u3002<\/li>\n\n\n\n
                                • \u5e26\u5bbd\u9700\u6c42\uff1a\u5355\u8f66\u95f4 BEV \u4f20\u8f93\u5e26\u5bbd\u4ece 82.5 MB\/s\uff08\u539f\u59cb\uff09\u964d\u81f3 0.32 MB\/s\uff08256\u00d7 \u538b\u7f29\uff09\uff0c\u652f\u6301 10 \u8f66\u534f\u540c\u573a\u666f\u5b9e\u65f6\u901a\u4fe1\u3002<\/li>\n<\/ul>\n\n\n\n

                                  4. \u53ef\u89c6\u5316\u5206\u6790\uff1a\u906e\u6321\u573a\u666f\u9884\u6d4b\u4f18\u52bf<\/strong><\/p>\n\n\n\n

                                  \u5982\u56fe\u6240\u793a\uff0c\u5355\u8f66\uff08\u7ea2\u8272\uff09\u611f\u77e5\u56e0\u89c6\u91ce\u53d7\u9650\u6f0f\u68c0\u53f3\u4fa7\u8f66\u8f86\uff0c\u5bfc\u81f4\u7f3a\u5c11\u5bf9\u4e8e\u4e00\u4e9b\u8f66\u8f86\u7684\u8f68\u8ff9\u3002\u800c CMP \u901a\u8fc7\u534f\u540c\u611f\u77e5\u8865\u5168\u88ab\u906e\u6321\u76ee\u6807\uff0c\u4e14\u9884\u6d4b\u8f68\u8ff9\u4e0e\u771f\u503c\u9ad8\u5ea6\u543b\u5408\u3002<\/p>\n\n\n\n

                                  \"\"\/<\/figure>\n\n\n\n

                                  \u56db\uff0e\u603b\u7ed3\uff1a\u534f\u540c\u9884\u6d4b\u5f00\u542f\u81ea\u52a8\u9a7e\u9a76\u65b0\u8303\u5f0f<\/strong><\/p>\n\n\n\n

                                  CMP \u901a\u8fc7\u611f\u77e5 – \u9884\u6d4b – \u805a\u5408\u5168\u94fe\u8def\u534f\u540c\uff0c\u5728\u901a\u4fe1\u6548\u7387\u3001\u5ef6\u8fdf\u9c81\u68d2\u6027\u3001\u9884\u6d4b\u7cbe\u5ea6\u4e09\u5927\u7ef4\u5ea6\u5b9e\u73b0\u7a81\u7834\uff0c\u4e3a\u590d\u6742\u52a8\u6001\u573a\u666f\u4e0b\u7684\u81ea\u52a8\u9a7e\u9a76\u63d0\u4f9b\u4e86\u53ef\u9760\u6280\u672f\u65b9\u6848\u3002\u672a\u6765\uff0c\u56e2\u961f\u5c06\u8fdb\u4e00\u6b65\u63a2\u7d22\u7aef\u5230\u7aef\u53ef\u5fae\u5206\u67b6\u6784\u4e0e\u591a\u6a21\u6001\u878d\u5408\uff0c\u63a8\u52a8\u534f\u540c\u81ea\u52a8\u9a7e\u9a76\u8fc8\u5411\u66f4\u9ad8\u9636\u667a\u80fd\u3002\u968f\u7740 V2X \u6280\u672f\u7684\u666e\u53ca\uff0cCMP \u6709\u671b\u6210\u4e3a\u4e0b\u4e00\u4ee3\u81ea\u52a8\u9a7e\u9a76\u7cfb\u7edf\u7684\u6838\u5fc3\u6a21\u5757\uff0c\u4e3a\u667a\u6167\u4ea4\u901a\u4e0e\u65e0\u4eba\u9a7e\u9a76\u89c4\u6a21\u5316\u843d\u5730\u5960\u5b9a\u6280\u672f\u57fa\u77f3\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  \u539f\u6587\u94fe\u63a5\uff1ahttps:\/\/www.163.co […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,1],"tags":[],"class_list":["post-26614","post","type-post","status-publish","format-standard","hentry","category-technology-frontier","category-home"],"views":0,"_links":{"self":[{"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/posts\/26614"}],"collection":[{"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/comments?post=26614"}],"version-history":[{"count":1,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/posts\/26614\/revisions"}],"predecessor-version":[{"id":26615,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/posts\/26614\/revisions\/26615"}],"wp:attachment":[{"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/media?parent=26614"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/categories?post=26614"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/tags?post=26614"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}