{"id":17914,"date":"2024-10-28T15:29:22","date_gmt":"2024-10-28T07:29:22","guid":{"rendered":"http:\/\/192.168.10.115\/?p=17914"},"modified":"2024-10-28T15:29:22","modified_gmt":"2024-10-28T07:29:22","slug":"2024-10-28-autoware%e4%b8%8eapollo%e5%a6%82%e4%bd%95%e5%8a%a9%e5%8a%9b%e6%99%ba%e8%83%bd%e7%bd%91%e8%81%94%e8%bd%a6%e8%b7%af%e5%8d%8f%e5%90%8c%e8%87%aa%e5%8a%a8%e9%a9%be%e9%a9%b6%e6%b2%99%e7%9b%98","status":"publish","type":"post","link":"http:\/\/222.128.65.89:18086\/index.php\/2024\/10\/28\/17914\/","title":{"rendered":"2024-10-28 Autoware\u4e0eApollo\u5982\u4f55\u52a9\u529b\u667a\u80fd\u7f51\u8054\u8f66\u8def\u534f\u540c\u81ea\u52a8\u9a7e\u9a76\u6c99\u76d8"},"content":{"rendered":"\n

\u5728\u667a\u80fd\u7f51\u8054\u8f66\u8def\u534f\u540c\u81ea\u52a8\u9a7e\u9a76\u9886\u57df\uff0cAutoware\u4e0eApollo\u7b97\u6cd5\u7684\u5f15\u5165\u4e3a\u6211\u4eec\u7684\u667a\u80fd\u7f51\u8054\u8f66\u8def\u534f\u540c\u81ea\u52a8\u9a7e\u9a76\u6c99\u76d8\u5e26\u6765\u4e86\u98a0\u8986\u6027\u7684\u9769\u65b0\u3002\u672c\u6587\u5c06\u6df1\u5165\u5256\u6790\u8fd9\u4e24\u4e2a\u5e73\u53f0\u5982\u4f55\u901a\u8fc7\u5148\u8fdb\u7684\u7b97\u6cd5\u4e0e\u6280\u672f\uff0c\u5b9e\u73b0\u4e86\u667a\u80fd\u7f51\u8054\u8f66\u8f86\u5728\u4eff\u771f\u73af\u5883\u4e2d\u7684\u9ad8\u5ea6\u81ea\u4e3b\u884c\u9a76\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
    \n
  1. \u9ad8\u7cbe\u5ea6\u5b9a\u4f4d
    Autoware\u9ad8\u7cbe\u5ea6\u5b9a\u4f4d\u7b97\u6cd5\u539f\u7406<\/li>\n<\/ol>\n\n\n\n

    Autoware\u7684\u9ad8\u7cbe\u5ea6\u5b9a\u4f4d\u662f\u57fa\u4e8e\u591a\u4f20\u611f\u5668\u878d\u5408\u7684\u5148\u8fdb\u7b97\u6cd5\u3002\u5176\u4e2d\uff0c\u6fc0\u5149\u96f7\u8fbe\u63d0\u4f9b\u4e86\u8f66\u8f86\u5468\u56f4\u73af\u5883\u7684\u9ad8\u7cbe\u5ea6\u7a7a\u95f4\u4fe1\u606f\uff0cIMU\u4f20\u611f\u5668\u8bb0\u5f55\u4e86\u8f66\u8f86\u7684\u59ff\u6001\u4fe1\u606f\uff0c\u800c\u8f6e\u901f\u8ba1\u5219\u63d0\u4f9b\u4e86\u8f66\u8f86\u8fd0\u52a8\u7684\u901f\u5ea6\u4fe1\u606f\u3002\u8fd9\u4e9b\u4f20\u611f\u5668\u6570\u636e\u901a\u8fc7\u6ee4\u6ce2\u4e0e\u878d\u5408\u7b97\u6cd5\u8fdb\u884c\u5904\u7406\uff0c\u6700\u7ec8\u5b9e\u73b0\u4e86\u8f66\u8f86\u5728\u4e09\u7ef4\u7a7a\u95f4\u7684\u7cbe\u786e\u5b9a\u4f4d\u3002<\/p>\n\n\n\n

    Autoware\u9ad8\u7cbe\u5ea6\u5b9a\u4f4d\u7b97\u6cd5\u4ee3\u7801\u793a\u4f8b<\/p>\n\n\n\n

    #include <iostream>\n#include <cmath>\n#include <Eigen\/Dense>\n \nclass HighPrecisionLocalization {\npublic:\n    HighPrecisionLocalization() {\n        \/\/ \u521d\u59cb\u5316\u72b6\u6001\u5411\u91cf\u548c\u534f\u65b9\u5dee\u77e9\u9635\n        x_ << 0, 0, 0, 0, 0, 0;\n        P_ << 1, 0, 0, 0, 0, 0,\n              0, 1, 0, 0, 0, 0,\n              0, 0, 1, 0, 0, 0,\n              0, 0, 0, 1, 0, 0,\n              0, 0, 0, 0, 1, 0,\n              0, 0, 0, 0, 0, 1;\n \n        \/\/ \u521d\u59cb\u5316\u8fc7\u7a0b\u566a\u58f0\u534f\u65b9\u5dee\u77e9\u9635\n        Q_ << 0.1, 0, 0, 0, 0, 0,\n              0, 0.1, 0, 0, 0, 0,\n              0, 0, 0.1, 0, 0, 0,\n              0, 0, 0, 0.1, 0, 0,\n              0, 0, 0, 0, 0.1, 0,\n              0, 0, 0, 0, 0, 0.1;\n \n        \/\/ \u521d\u59cb\u5316\u6d4b\u91cf\u566a\u58f0\u534f\u65b9\u5dee\u77e9\u9635\n        R_ << 0.1, 0, 0, 0, 0, 0,\n              0, 0.1, 0, 0, 0, 0,\n              0, 0, 0.1, 0, 0, 0,\n              0, 0, 0, 0.1, 0, 0,\n              0, 0, 0, 0, 0.1, 0,\n              0, 0, 0, 0, 0, 0.1;\n    }\n \n    \/\/ \u66f4\u65b0\u72b6\u6001\u548c\u534f\u65b9\u5dee\n    void update(const Eigen::VectorXd& z, const Eigen::VectorXd& u) {\n        \/\/ \u9884\u6d4b\u6b65\u9aa4\n        predict(u);\n \n        \/\/ \u66f4\u65b0\u6b65\u9aa4\n        correct(z);\n    }\n \n    \/\/ \u83b7\u53d6\u5f53\u524d\u72b6\u6001\n    Eigen::VectorXd getState() const {\n        return x_;\n    }\n \nprivate:\n    Eigen::VectorXd x_;  \/\/ \u72b6\u6001\u5411\u91cf\n    Eigen::MatrixXd P_;  \/\/ \u534f\u65b9\u5dee\u77e9\u9635\n    Eigen::MatrixXd Q_;  \/\/ \u8fc7\u7a0b\u566a\u58f0\u534f\u65b9\u5dee\u77e9\u9635\n    Eigen::MatrixXd R_;  \/\/ \u6d4b\u91cf\u566a\u58f0\u534f\u65b9\u5dee\u77e9\u9635\n \n    void predict(const Eigen::VectorXd& u) {\n        \/\/ \u9884\u6d4b\u72b6\u6001\n        x_ = A * x_ + B * u;\n \n        \/\/ \u9884\u6d4b\u534f\u65b9\u5dee\n        P_ = A * P_ * A.transpose() + Q_;\n    }\n \n    void correct(const Eigen::VectorXd& z) {\n        \/\/ \u8ba1\u7b97\u5361\u5c14\u66fc\u589e\u76ca\n        Eigen::MatrixXd K = P_ * H.transpose() * (H * P_ * H.transpose() + R_).inverse();\n \n        \/\/ \u66f4\u65b0\u72b6\u6001\n        x_ = x_ + K * (z - H * x_);\n \n        \/\/ \u66f4\u65b0\u534f\u65b9\u5dee\n        P_ = (Eigen::MatrixXd::Identity(6, 6) - K * H) * P_;\n    }\n};\n \nint main() {\n    HighPrecisionLocalization localization;\n \n    \/\/ \u793a\u4f8b\uff1a\u6a21\u62df\u6d4b\u91cf\u548c\u63a7\u5236\u8f93\u5165\n    Eigen::VectorXd z(6);  \/\/ \u6d4b\u91cf\n    Eigen::VectorXd u(6);  \/\/ \u63a7\u5236\u8f93\u5165\n \n    z << 1, 1, 1, 1, 1, 1;  \/\/ \u6a21\u62df\u6d4b\u91cf\u503c\n    u << 0.1, 0.1, 0.1, 0.1, 0.1, 0.1;  \/\/ \u6a21\u62df\u63a7\u5236\u8f93\u5165\n \n    \/\/ \u66f4\u65b0\u72b6\u6001\n    localization.update(z, u);\n \n    \/\/ \u83b7\u53d6\u5f53\u524d\u72b6\u6001\n    Eigen::VectorXd state = localization.getState();\n    std::cout << \"Current State: \" << state.transpose() << std::endl;\n \n    return 0;\n}<\/code><\/pre>\n\n\n\n
    \u8fd9\u53ea\u662f\u4e00\u4e2a\u975e\u5e38\u57fa\u7840\u7684\u793a\u4f8b\uff0c\u5b9e\u9645\u4e2d\u9700\u8981\u6839\u636e\u5177\u4f53\u573a\u666f\u548c\u4f20\u611f\u5668\u6570\u636e\u8fdb\u884c\u8c03\u6574\u548c\u4f18\u5316\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u66f4\u590d\u6742\u7684\u6a21\u578b\u548c\u7b97\u6cd5\uff0c\u4ee5\u6ee1\u8db3\u9ad8\u7cbe\u5ea6\u5b9a\u4f4d\u7684\u8981\u6c42\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    2. Autoware\u8def\u5f84\u89c4\u5212<\/strong><\/p>\n\n\n\n
    Autoware\u8def\u5f84\u89c4\u5212\u7b97\u6cd5\u539f\u7406<\/p>\n\n\n\n
    Autoware\u7684\u8def\u5f84\u89c4\u5212\u7b97\u6cd5\u57fa\u4e8e\u9ad8\u7cbe\u5ea6\u5730\u56fe\u6570\u636e\u3002\u901a\u8fc7\u5148\u8fdb\u7684\u5730\u56fe\u6784\u5efa\u6280\u672f\uff0c\u7cfb\u7edf\u80fd\u591f\u611f\u77e5\u57ce\u5e02\u9053\u8def\u7684\u8be6\u7ec6\u4fe1\u606f\uff0c\u5305\u62ec\u8f66\u9053\u3001\u4ea4\u53c9\u53e3\u3001\u505c\u8f66\u573a\u7b49\u3002\u8def\u5f84\u89c4\u5212\u7b97\u6cd5\u901a\u8fc7\u7efc\u5408\u8003\u8651\u8f66\u8f86\u5f53\u524d\u72b6\u6001\u3001\u76ee\u6807\u4f4d\u7f6e\u4ee5\u53ca\u5730\u56fe\u4fe1\u606f\uff0c\u751f\u6210\u4e00\u6761\u5b89\u5168\u3001\u9ad8\u6548\u7684\u884c\u8f66\u8def\u5f84\u3002<\/p>\n\n\n\n
    Autoware\u8def\u5f84\u89c4\u5212\u7b97\u6cd5\u4ee3\u7801\u793a\u4f8b<\/p>\n\n\n\n
    #include <iostream>\n#include <vector>\n#include <queue>\n#include <cmath>\n \nstruct Node {\n    int x, y;           \/\/ \u5750\u6807\n    double g, h;         \/\/ g: \u4ece\u8d77\u70b9\u5230\u5f53\u524d\u70b9\u7684\u4ee3\u4ef7\uff0ch: \u4ece\u5f53\u524d\u70b9\u5230\u76ee\u6807\u70b9\u7684\u4f30\u8ba1\u4ee3\u4ef7\n    Node* parent;        \/\/ \u7236\u8282\u70b9\u6307\u9488\n \n    Node(int x, int y, double g, double h, Node* parent)\n        : x(x), y(y), g(g), h(h), parent(parent) {}\n \n    \/\/ \u8ba1\u7b97\u603b\u4ee3\u4ef7 f = g + h\n    double getF() const {\n        return g + h;\n    }\n};\n \n\/\/ \u7528\u4e8e\u6bd4\u8f83\u8282\u70b9\u7684\u4f18\u5148\u961f\u5217\u6bd4\u8f83\u51fd\u6570\nstruct CompareNode {\n    bool operator()(const Node* a, const Node* b) const {\n        return a->getF() > b->getF();\n    }\n};\n \nclass AStarPlanner {\npublic:\n    AStarPlanner(const std::vector<std::vector<int>>& grid)\n        : grid(grid), rows(grid.size()), cols(grid[0].size()) {}\n \n    std::vector<std::pair<int, int>> plan(int start_x, int start_y, int goal_x, int goal_y) {\n        std::priority_queue<Node*, std::vector<Node*>, CompareNode> open_set;\n        std::vector<std::vector<bool>> closed_set(rows, std::vector<bool>(cols, false));\n \n        Node* start_node = new Node(start_x, start_y, 0.0, heuristic(start_x, start_y, goal_x, goal_y), nullptr);\n        open_set.push(start_node);\n \n        while (!open_set.empty()) {\n            Node* current = open_set.top();\n            open_set.pop();\n \n            if (current->x == goal_x && current->y == goal_y) {\n                \/\/ \u627e\u5230\u8def\u5f84\uff0c\u8fd4\u56de\u8def\u5f84\u5750\u6807\n                return extractPath(current);\n            }\n \n            closed_set[current->x][current->y] = true;\n \n            for (const auto& neighbor : getNeighbors(current->x, current->y)) {\n                if (!closed_set[neighbor.first][neighbor.second]) {\n                    double tentative_g = current->g + distance(current->x, current->y, neighbor.first, neighbor.second);\n                    Node* neighbor_node = findNode(open_set, neighbor.first, neighbor.second);\n \n                    if (neighbor_node == nullptr || tentative_g < neighbor_node->g) {\n                        if (neighbor_node == nullptr) {\n                            neighbor_node = new Node(neighbor.first, neighbor.second, 0.0, 0.0, nullptr);\n                        }\n \n                        neighbor_node->parent = current;\n                        neighbor_node->g = tentative_g;\n                        neighbor_node->h = heuristic(neighbor.first, neighbor.second, goal_x, goal_y);\n \n                        if (std::find(open_set.c.begin(), open_set.end(), neighbor_node) == open_set.end()) {\n                            open_set.push(neighbor_node);\n                        }\n                    }\n                }\n            }\n        }\n \n        return {};  \/\/ \u672a\u627e\u5230\u8def\u5f84\n    }\n \nprivate:\n    const std::vector<std::vector<int>>& grid;\n    const int rows, cols;\n \n    double heuristic(int x1, int y1, int x2, int y2) const {\n        \/\/ \u66fc\u54c8\u987f\u8ddd\u79bb\n        return std::abs(x1 - x2) + std::abs(y1 - y2);\n    }\n \n    double distance(int x1, int y1, int x2, int y2) const {\n        \/\/ \u76f4\u7ebf\u8ddd\u79bb\n        return std::sqrt(std::pow(x2 - x1, 2) + std::pow(y2 - y1, 2));\n    }\n \n    std::vector<Node*> getNeighbors(int x, int y) const {\n        \/\/ \u83b7\u53d6\u76f8\u90bb\u8282\u70b9\n        std::vector<std::pair<int, int>> neighbors;\n \n        \/\/ \u8fd9\u91cc\u6839\u636e\u5177\u4f53\u60c5\u51b5\u6dfb\u52a0\u76f8\u90bb\u8282\u70b9\n \n        return neighbors;\n    }\n \n    std::vector<std::pair<int, int>> extractPath(Node* node) const {\n        \/\/ \u63d0\u53d6\u8def\u5f84\u5750\u6807\n        std::vector<std::pair<int, int>> path;\n \n        while (node != nullptr) {\n            path.emplace_back(node->x, node->y);\n            node = node->parent;\n        }\n \n        std::reverse(path.begin(), path.end());\n        return path;\n    }\n \n    Node* findNode(const std::priority_queue<Node*, std::vector<Node*>, CompareNode>& nodes, int x, int y) const {\n        \/\/ \u5728\u4f18\u5148\u961f\u5217\u4e2d\u67e5\u627e\u8282\u70b9\n        for (Node* node : nodes) {\n            if (node->x == x && node->y == y) {\n                return node;\n            }\n        }\n        return nullptr;\n    }\n};\n \n\/\/ \u793a\u4f8b\u7528\u6cd5\nint main() {\n    std::vector<std::vector<int>> grid = {\n        {0, 0, 0, 0, 0},\n        {0, 1, 1, 0, 0},\n        {0, 0, 0, 1, 0},\n        {0, 0, 0, 1, 0},\n        {0, 0, 0, 0, 0}\n    };\n \n    AStarPlanner planner(grid);\n    std::vector<std::pair<int, int>> path = planner.plan(0, 0, 4, 4);\n \n    for (const auto& point : path) {\n        std::cout << \"(\" << point.first << \", \" << point.second << \") \";\n    }\n \n    return 0;\n}<\/code><\/pre>\n\n\n\n
    \u5b9e\u9645\u4e2d\u53ef\u80fd\u9700\u8981\u6839\u636e\u5177\u4f53\u573a\u666f\u8fdb\u884c\u8c03\u6574\u548c\u4f18\u5316\u3002\u5982\u679c\u60a8\u6709\u7279\u5b9a\u7684\u9700\u6c42\u6216\u7ea6\u675f\u6761\u4ef6\uff0c\u8bf7\u63d0\u4f9b\u66f4\u591a\u8be6\u7ec6\u4fe1\u606f\uff0c\u4ee5\u4fbf\u6211\u80fd\u591f\u8fdb\u4e00\u6b65\u8c03\u6574\u4ee3\u7801\u3002<\/p>\n\n\n\n
    3. Autoware\u9ad8\u7cbe\u5ea6\u5730\u56fe\u6784\u5efa\u4e0e\u5904\u7406<\/strong><\/p>\n\n\n\n
    Autoware\u9ad8\u7cbe\u5ea6\u5730\u56fe\u6784\u5efa\u4e0e\u5904\u7406\u7b97\u6cd5\u539f\u7406<\/p>\n\n\n\n
    Autoware\u901a\u8fc7\u8f66\u8f7d\u6fc0\u5149\u96f7\u8fbe\u3001\u6444\u50cf\u5934\u7b49\u4f20\u611f\u5668\u83b7\u53d6\u9053\u8def\u4fe1\u606f\uff0c\u5e76\u5229\u7528SLAM\uff08Simultaneous Localization and Mapping\uff09\u6280\u672f\u6784\u5efa\u9ad8\u7cbe\u5ea6\u5730\u56fe\u3002\u8fd9\u4e00\u5730\u56fe\u4e0d\u4ec5\u5305\u542b\u9053\u8def\u7684\u51e0\u4f55\u5f62\u72b6\uff0c\u8fd8\u5305\u62ec\u8f66\u9053\u7ebf\u3001\u4ea4\u53c9\u53e3\u3001\u505c\u8f66\u4f4d\u7b49\u8be6\u7ec6\u4fe1\u606f\u3002\u7b97\u6cd5\u901a\u8fc7\u5bf9\u8fd9\u4e9b\u4fe1\u606f\u7684\u5904\u7406\uff0c\u751f\u6210\u9002\u7528\u4e8e\u8def\u5f84\u89c4\u5212\u7684\u9ad8\u7cbe\u5ea6\u5730\u56fe\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    Autoware\u9ad8\u7cbe\u5ea6\u5730\u56fe\u6784\u5efa\u4e0e\u5904\u7406\u7b97\u6cd5\u4ee3\u7801\u793a\u4f8b<\/h6>\n\n\n\n
    class AutowareMapConstruction:\n    def __init__(self, lidar_data, camera_data):\n        self.lidar_data = lidar_data\n        self.camera_data = camera_data\n \n    def build_high_precision_map(self):\n        # \u5229\u7528\u6fc0\u5149\u96f7\u8fbe\u548c\u6444\u50cf\u5934\u6570\u636e\u8fdb\u884c\u5730\u56fe\u6784\u5efa\n        # ...\n \n    def process_map_data(self):\n        # \u5bf9\u6784\u5efa\u7684\u5730\u56fe\u6570\u636e\u8fdb\u884c\u5904\u7406\uff0c\u63d0\u53d6\u8f66\u9053\u7ebf\u3001\u4ea4\u53c9\u53e3\u7b49\u4fe1\u606f\n        # ...\n        return processed_map_data<\/code><\/pre>\n\n\n\n
    \u4e0a\u8ff0\u4ee3\u7801\u7247\u6bb5\u5c55\u793a\u4e86Autoware\u9ad8\u7cbe\u5ea6\u5730\u56fe\u6784\u5efa\u4e0e\u5904\u7406\u7b97\u6cd5\u7684\u7b80\u8981\u5b9e\u73b0\u3002\u5b9e\u9645\u4e2d\uff0c\u8fd9\u53ef\u80fd\u6d89\u53ca\u5230\u66f4\u591a\u7684\u4f20\u611f\u5668\u878d\u5408\u6280\u672f\u3001\u56fe\u50cf\u5904\u7406\u7b49\u590d\u6742\u7b97\u6cd5\u3002<\/p>\n\n\n\n
      \n
    1. Apollo\u8f66\u8f86\u51b3\u7b56\u4e0e\u63a7\u5236
      Apollo\u8f66\u8f86\u51b3\u7b56\u4e0e\u63a7\u5236\u7b97\u6cd5\u539f\u7406<\/li>\n<\/ol>\n\n\n\n
      Apollo\u5728\u8f66\u8f86\u51b3\u7b56\u4e0e\u63a7\u5236\u65b9\u9762\u91c7\u7528\u4e86\u5148\u8fdb\u7684\u6a21\u578b\u9884\u6d4b\u63a7\u5236\uff08Model Predictive Control, MPC\uff09\u7b97\u6cd5\u3002\u8be5\u7b97\u6cd5\u901a\u8fc7\u5bf9\u8f66\u8f86\u52a8\u529b\u5b66\u6a21\u578b\u7684\u5efa\u6a21\uff0c\u5e76\u7ed3\u5408\u73af\u5883\u611f\u77e5\u4fe1\u606f\uff0c\u5b9e\u65f6\u4f18\u5316\u8f66\u8f86\u7684\u63a7\u5236\u7b56\u7565\uff0c\u4ee5\u786e\u4fdd\u8f66\u8f86\u5728\u590d\u6742\u8def\u51b5\u4e0b\u80fd\u591f\u505a\u51fa\u5b89\u5168\u800c\u9ad8\u6548\u7684\u9a7e\u9a76\u51b3\u7b56\u3002<\/p>\n\n\n\n
      Apollo\u8f66\u8f86\u51b3\u7b56\u4e0e\u63a7\u5236\u7b97\u6cd5\u4ee3\u7801\u793a\u4f8b<\/p>\n\n\n\n
      class ApolloControl {\npublic:\n    ApolloControl(const VehicleState& current_state, const EnvironmentInfo& env_info) \n        : current_state_(current_state), env_info_(env_info) {}\n \n    void model_predictive_control() {\n        \/\/ \u6a21\u578b\u9884\u6d4b\u63a7\u5236\u7b97\u6cd5\u7684\u5b9e\u73b0\n        \/\/ ...\n        adjust_vehicle_controls();  \/\/ \u6839\u636e\u4f18\u5316\u7ed3\u679c\u8c03\u6574\u8f66\u8f86\u63a7\u5236\u7b56\u7565\n    }\n \nprivate:\n    VehicleState current_state_;  \/\/ \u5f53\u524d\u8f66\u8f86\u72b6\u6001\n    EnvironmentInfo env_info_;    \/\/ \u5f53\u524d\u73af\u5883\u611f\u77e5\u4fe1\u606f\n \n    void adjust_vehicle_controls() {\n        \/\/ \u6839\u636e\u4f18\u5316\u7ed3\u679c\u8c03\u6574\u8f66\u8f86\u7684\u63a7\u5236\u7b56\u7565\n        \/\/ ...\n    }\n};<\/code><\/pre>\n\n\n\n
      \u4e0a\u8ff0\u4ee3\u7801\u6f14\u793a\u4e86Apollo\u8f66\u8f86\u51b3\u7b56\u4e0e\u63a7\u5236\u7b97\u6cd5\u7684\u57fa\u672c\u7ed3\u6784\u3002\u5b9e\u9645\u4e2d\uff0cMPC\u7b97\u6cd5\u53ef\u80fd\u5305\u542b\u66f4\u591a\u7684\u72b6\u6001\u9884\u6d4b\u3001\u76ee\u6807\u4f18\u5316\u7b49\u590d\u6742\u6b65\u9aa4\u3002<\/p>\n\n\n\n
      \u901a\u8fc7\u6df1\u5165\u5206\u6790Autoware\u7684\u9ad8\u7cbe\u5ea6\u5730\u56fe\u6784\u5efa\u4e0e\u5904\u7406\u548cApollo\u7684\u8f66\u8f86\u51b3\u7b56\u4e0e\u63a7\u5236\uff0c\u6211\u4eec\u66f4\u5168\u9762\u5730\u4e86\u89e3\u4e86\u8fd9\u4e24\u4e2a\u5e73\u53f0\u5982\u4f55\u5728\u81ea\u52a8\u9a7e\u9a76\u6c99\u76d8\u4e2d\u534f\u540c\u5de5\u4f5c\uff0c\u5b9e\u73b0\u667a\u80fd\u7f51\u8054\u8f66\u8def\u534f\u540c\u7684\u613f\u666f\u3002<\/p>\n\n\n\n
        \n
      1. Autoware\u8f66\u8f86\u611f\u77e5\u4e0e\u73af\u5883\u611f\u77e5
        Autoware\u8f66\u8f86\u611f\u77e5\u4e0e\u73af\u5883\u611f\u77e5\u7b97\u6cd5\u539f\u7406<\/li>\n<\/ol>\n\n\n\n
        Autoware\u5728\u8f66\u8f86\u611f\u77e5\u4e0e\u73af\u5883\u611f\u77e5\u65b9\u9762\u91c7\u7528\u4e86\u591a\u4f20\u611f\u5668\u878d\u5408\u6280\u672f\u3002\u6fc0\u5149\u96f7\u8fbe\u3001\u6444\u50cf\u5934\u7b49\u4f20\u611f\u5668\u5b9e\u65f6\u83b7\u53d6\u5468\u56f4\u9053\u8def\u7684\u51e0\u4f55\u548c\u8bed\u4e49\u4fe1\u606f\uff0c\u901a\u8fc7\u7b97\u6cd5\u878d\u5408\u5f97\u5230\u5168\u9762\u800c\u7cbe\u51c6\u7684\u73af\u5883\u611f\u77e5\u7ed3\u679c\u3002\u8fd9\u4e00\u7ed3\u679c\u5bf9\u4e8e\u8f66\u8f86\u7684\u5b89\u5168\u9a7e\u9a76\u548c\u51b3\u7b56\u63d0\u4f9b\u4e86\u575a\u5b9e\u7684\u57fa\u7840\u3002
        Autoware\u8f66\u8f86\u611f\u77e5\u4e0e\u73af\u5883\u611f\u77e5\u7b97\u6cd5\u4ee3\u7801\u793a\u4f8b<\/p>\n\n\n\n
        class AutowarePerception:\n    def __init__(self, lidar_data, camera_data):\n        self.lidar_data = lidar_data\n        self.camera_data = camera_data\n \n    def perceive_environment(self):\n        # \u5229\u7528\u591a\u4f20\u611f\u5668\u6570\u636e\u8fdb\u884c\u73af\u5883\u611f\u77e5\n        # ...\n \n    def integrate_semantic_information(self):\n        # \u6574\u5408\u8bed\u4e49\u4fe1\u606f\uff0c\u5982\u4ea4\u901a\u6807\u5fd7\u3001\u8f66\u8f86\u3001\u884c\u4eba\u7b49\n        # ...\n        return integrated_info<\/code><\/pre>\n\n\n\n
        \u4e0a\u8ff0\u4ee3\u7801\u4ec5\u662fAutoware\u8f66\u8f86\u611f\u77e5\u4e0e\u73af\u5883\u611f\u77e5\u7b97\u6cd5\u7684\u7b80\u5355\u793a\u4f8b\uff0c\u5b9e\u9645\u4e2d\u53ef\u80fd\u4f1a\u6d89\u53ca\u5230\u66f4\u591a\u590d\u6742\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u548c\u5b9e\u65f6\u5904\u7406\u7b97\u6cd5\u3002<\/p>\n\n\n\n
        6. Apollo\u9ad8\u7ea7\u9a7e\u9a76\u8f85\u52a9\u7cfb\u7edf<\/strong><\/p>\n\n\n\n
        Apollo\u9ad8\u7ea7\u9a7e\u9a76\u8f85\u52a9\u7cfb\u7edf\u7b97\u6cd5\u539f\u7406<\/p>\n\n\n\n
        Apollo\u7684\u9ad8\u7ea7\u9a7e\u9a76\u8f85\u52a9\u7cfb\u7edf\u662f\u57fa\u4e8e\u5148\u8fdb\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u6280\u672f\u548c\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\u7684\u3002\u901a\u8fc7\u5bf9\u9053\u8def\u4e0a\u7684\u5404\u79cd\u4ea4\u901a\u6807\u5fd7\u3001\u8f66\u8f86\u3001\u884c\u4eba\u7b49\u8fdb\u884c\u5b9e\u65f6\u8bc6\u522b\u548c\u8ddf\u8e2a\uff0c\u4e3a\u9a7e\u9a76\u5458\u63d0\u4f9b\u5168\u9762\u7684\u73af\u5883\u8ba4\u77e5\u3002\u8be5\u7cfb\u7edf\u8fd8\u5177\u5907\u9884\u6d4b\u7b97\u6cd5\uff0c\u80fd\u591f\u9884\u6d4b\u5176\u4ed6\u8f66\u8f86\u548c\u884c\u4eba\u7684\u884c\u4e3a\uff0c\u4e3a\u9a7e\u9a76\u5458\u63d0\u4f9b\u66f4\u4e3a\u5b89\u5168\u7684\u9a7e\u9a76\u4f53\u9a8c\u3002<\/p>\n\n\n\n
        Apollo\u9ad8\u7ea7\u9a7e\u9a76\u8f85\u52a9\u7cfb\u7edf\u7b97\u6cd5\u4ee3\u7801\u793a\u4f8b<\/p>\n\n\n\n
        class ApolloADAS {\npublic:\n    ApolloADAS(const CameraData& camera_data) : camera_data_(camera_data) {}\n \n    void object_detection_tracking() {\n        \/\/ \u5bf9\u6444\u50cf\u5934\u6570\u636e\u8fdb\u884c\u76ee\u6807\u68c0\u6d4b\u548c\u8ddf\u8e2a\n        \/\/ ...\n    }\n \n    void behavior_prediction() {\n        \/\/ \u9884\u6d4b\u5176\u4ed6\u8f66\u8f86\u548c\u884c\u4eba\u7684\u884c\u4e3a\n        \/\/ ...\n    }\n \nprivate:\n    CameraData camera_data_;  \/\/ \u6444\u50cf\u5934\u6570\u636e\n};<\/code><\/pre>\n\n\n\n
        \u4e0a\u8ff0\u4ee3\u7801\u5c55\u793a\u4e86Apollo\u9ad8\u7ea7\u9a7e\u9a76\u8f85\u52a9\u7cfb\u7edf\u4e2d\u90e8\u5206\u529f\u80fd\u7684\u7b80\u8981\u5b9e\u73b0\u3002\u5b9e\u9645\u4e2d\uff0c\u8fd9\u53ef\u80fd\u6d89\u53ca\u5230\u66f4\u4e3a\u5e9e\u5927\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u548c\u5927\u89c4\u6a21\u7684\u8bad\u7ec3\u6570\u636e\u3002<\/p>\n\n\n\n
        \u603b\u7ed3<\/p>\n\n\n\n
        \u901a\u8fc7\u5bf9Autoware\u4e0eApollo\u5728\u667a\u80fd\u7f51\u8054\u8f66\u8def\u534f\u540c\u81ea\u52a8\u9a7e\u9a76\u6c99\u76d8\u4e2d\u7684\u534f\u540c\u8868\u73b0\u8fdb\u884c\u5168\u9762\u6df1\u5165\u7684\u5256\u6790\uff0c\u6211\u4eec\u53d1\u73b0\u8fd9\u4e24\u4e2a\u5e73\u53f0\u901a\u8fc7\u5148\u8fdb\u7684\u7b97\u6cd5\u4e0e\u6280\u672f\uff0c\u4e3a\u5b9e\u73b0\u81ea\u52a8\u9a7e\u9a76\u63d0\u4f9b\u4e86\u575a\u5b9e\u7684\u6280\u672f\u652f\u6301\u3002\u4ece\u9ad8\u7cbe\u5ea6\u5730\u56fe\u6784\u5efa\u5230\u8def\u5f84\u89c4\u5212\u3001\u8f66\u8f86\u51b3\u7b56\u4e0e\u63a7\u5236\uff0c\u518d\u5230\u8f66\u8f86\u611f\u77e5\u4e0e\u73af\u5883\u611f\u77e5\uff0c\u4ee5\u53ca\u9ad8\u7ea7\u9a7e\u9a76\u8f85\u52a9\u7cfb\u7edf\uff0cAutoware\u4e0eApollo\u7684\u5f3a\u5f3a\u8054\u624b\uff0c\u4e3a\u667a\u80fd\u7f51\u8054\u8f66\u8def\u534f\u540c\u7684\u672a\u6765\u5960\u5b9a\u4e86\u575a\u5b9e\u7684\u57fa\u7840\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
        \u5728\u667a\u80fd\u7f51\u8054\u8f66\u8def\u534f\u540c\u81ea\u52a8\u9a7e\u9a76\u9886\u57df\uff0cAutowar […]<\/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-17914","post","type-post","status-publish","format-standard","hentry","category-technology-frontier","category-home"],"views":1,"_links":{"self":[{"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/posts\/17914"}],"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=17914"}],"version-history":[{"count":1,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/posts\/17914\/revisions"}],"predecessor-version":[{"id":17918,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/posts\/17914\/revisions\/17918"}],"wp:attachment":[{"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/media?parent=17914"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/categories?post=17914"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/222.128.65.89:18086\/index.php\/wp-json\/wp\/v2\/tags?post=17914"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}