【Apollo自动驾驶源码解读】车道线的感知和高精地图融合【Apollo自动驾驶源码解读】车

模式选择在modules/map/relative_map/conf/relative_map_config.pb.txt文件中对模式进行修改：

lane_source: OFFLINE_GENERATED

存在两种模式：OFFLINE_GENERATED 和 PERCPTION
当使用前者时高精地图和感知进行融合
使用后者时只是用感知生成相对地图
本文只讨论前者
函数入口文件路径：modules/map/relative_map/navigation_lane.cc
函数名：bool NavigationLane::GeneratePath() 这个是重点分析的函数
下面进入这个函数
逻辑分析在这里进行了模式检查：

if (config_.lane_source() == NavigationLaneConfig::OFFLINE_GENERATED && navigation_line_num > 0)

现在进入这个if中

for (int i = 0; i < navigation_line_num; ++i) { auto current_navi_path = std::make_shared(); auto *path = current_navi_path->mutable_path(); if (ConvertNavigationLineToPath(i, path)) { current_navi_path->set_path_priority( navigation_info_.navigation_path(i).path_priority()); navigation_path_list_.emplace_back( i, default_left_width_, default_right_width_, current_navi_path); } }

这里根据导航数据对 navigation_path_list_ 进行了填充接下来马上进行判断：

if (navigation_path_list_.empty()) { generate_path_on_perception_func(); return true; }

如果没有数据只能使用感知了
如果数据不为空我们继续
这里对导航路线进行排序还有设置当前车辆所在路径
其中 current_navi_path_tuple_ 这个变量记录了当前车所在的道路的索引下面我们将这个变量称为当前车道

//根据车的方向将导航路线从左到右排序 navigation_path_list_.sort( [](const NaviPathTuple &left, const NaviPathTuple &right) { double left_y = std::get<3>(left)->path().path_point(0).y(); double right_y = std::get<3>(right)->path().path_point(0).y(); return left_y > right_y; }); // Get which navigation path the vehicle is currently on. double min_d = std::numeric_limits::max(); for (const auto &navi_path_tuple : navigation_path_list_) { int current_line_index = std::get<0>(navi_path_tuple); ADEBUG << "Current navigation path index is: " << current_line_index; double current_d = std::numeric_limits::max(); auto item_iter = last_project_index_map_.find(current_line_index); if (item_iter != last_project_index_map_.end()) { current_d = item_iter->second.second; } if (current_d < min_d) { min_d = current_d; //设置当前车辆所在路径 current_navi_path_tuple_ = navi_path_tuple; } }

接下来将当前车道进行融合而不是将所有车道进行融合！

//融合感知和导航车道线 // Merge current navigation path where the vehicle is located with perceived // lane markers. auto *path = std::get<3>(current_navi_path_tuple_)->mutable_path(); MergeNavigationLineAndLaneMarker(std::get<0>(current_navi_path_tuple_), path);

进入融合函数

void NavigationLane::MergeNavigationLineAndLaneMarker(const int line_index, common::Path *const path)

进入之后直接进行判断
如果点的数量少于2 则首先生成导航的车道线

if (path->path_point_size() < 2) { path->Clear(); ConvertNavigationLineToPath(line_index, path); }

如果还是点的数量过少那只能使用感知的了并直接return 不用融合了

//如果路径点还是小于2 则根据感知生成导航车道线 // If the size of "path" points is still smaller than 2, just generate a // navigation path based on perceived lane markers. if (path->path_point_size() < 2) { path->Clear(); ConvertLaneMarkerToPath(perception_obstacles_.lane_marker(), path); return; }common::Path lane_marker_path; ConvertLaneMarkerToPath(perception_obstacles_.lane_marker(), &lane_marker_path); // If the size of lane marker path points is smaller than 2, merging is not // required. if (lane_marker_path.path_point_size() < 2) { return; }

下面进行融合
前提是只对当前车道进行车道线的融合因为传入的就只是当前车道
融合原理大概是预先设置一个权重目前感觉这个权重应该是不变的这里存个疑
然后遍历导航中这条线每个点在导航中找到这个点对应的点根据权重进行融合
代码展示

int lane_marker_index = 0; double navigation_line_weight = 1.0 - config_.lane_marker_weight(); for (int i = 0; i < path->path_point_size(); ++i) { auto *point = path->mutable_path_point(i); double s = point->s(); //计算s距离之后的匹配点并返回匹配点的索引 //lane_marker_path为感知发来的道路 auto lane_maker_point = GetPathPointByS(lane_marker_path, lane_marker_index, s, &lane_marker_index); // For the beginning and ending portions of a navigation path beyond the // perceived path, only the y-coordinates in the FLU coordinate system are // used for merging. const int marker_size = lane_marker_path.path_point_size(); if (lane_marker_index < 0 || lane_marker_index > (marker_size - 1)) { point->set_y(navigation_line_weight * point->y() + (1 - navigation_line_weight) * lane_maker_point.y()); lane_marker_index = 0; continue; } *point = common::util::GetWeightedAverageOfTwoPathPoints( *point, lane_maker_point, navigation_line_weight, 1 - navigation_line_weight); }

【【Apollo自动驾驶源码解读】车道线的感知和高精地图融合】然后回到之前的函数
下面就是各种设置宽度了
先提取了感知的道路宽度
遍历导航中所有的道路（这里不仅仅是处理当前道路）
如果遍历到了当前道路就设置为感知到的道路宽度
代码展示

//设置导航道路的宽度 // Set the width for the navigation path which the vehicle is currently on. //注意感知车道线宽度是在这里储存的 double left_width = perceived_left_width_ > 0.0 ? perceived_left_width_ : default_left_width_; double right_width = perceived_right_width_ > 0.0 ? perceived_right_width_ : default_right_width_; if (!IsFloatEqual(left_width, default_left_width_) && !IsFloatEqual(right_width, default_right_width_)) { left_width = left_width > default_left_width_ ? left_width - min_d : left_width + min_d; right_width = right_width > default_right_width_ ? right_width - min_d : right_width + min_d; }ADEBUG << "The left width of current lane is: " << left_width << " and the right width of current lane is: " << right_width; std::get<1>(current_navi_path_tuple_) = left_width; std::get<2>(current_navi_path_tuple_) = right_width; auto curr_navi_path_iter = std::find_if( std::begin(navigation_path_list_), std::end(navigation_path_list_), [this](const NaviPathTuple &item) { return std::get<0>(item) == std::get<0>(current_navi_path_tuple_); }); if (curr_navi_path_iter != std::end(navigation_path_list_)) { std::get<1>(*curr_navi_path_iter) = left_width; std::get<2>(*curr_navi_path_iter) = right_width; } // Set the width between each navigation path and its adjacent path. // The reason for using average of multiple points is to prevent too much // interference from a singularity. // If current navigation path is the path which the vehicle is currently // on, the current lane width uses the perceived width. //设置每个导航路径与其相邻路径之间的宽度。 //使用多个点的平均值是为了防止奇点产生过多干扰。 //如果当前导航路径是车辆当前所在的路径，则当前车道宽度使用感知宽度。 int average_point_size = 5; for (auto iter = navigation_path_list_.begin(); iter != navigation_path_list_.end(); ++iter) { const auto &curr_path = std::get<3>(*iter)->path(); // Left neighbor auto prev_iter = std::prev(iter); if (prev_iter != navigation_path_list_.end()) { const auto &prev_path = std::get<3>(*prev_iter)->path(); average_point_size = std::min( average_point_size, std::min(curr_path.path_point_size(), prev_path.path_point_size())); double lateral_distance_sum = 0.0; for (int i = 0; i < average_point_size; ++i) { lateral_distance_sum += fabs(curr_path.path_point(i).y() - prev_path.path_point(i).y()); } double width = lateral_distance_sum / static_cast(average_point_size) / 2.0; width = common::math::Clamp(width, config_.min_lane_half_width(), config_.max_lane_half_width()); auto &curr_left_width = std::get<1>(*iter); auto &prev_right_width = std::get<2>(*prev_iter); if (std::get<0>(*iter) == std::get<0>(current_navi_path_tuple_)) { prev_right_width = 2.0 * width - curr_left_width; } else { curr_left_width = width; prev_right_width = width; } } // Right neighbor auto next_iter = std::next(iter); if (next_iter != navigation_path_list_.end()) { const auto &next_path = std::get<3>(*next_iter)->path(); average_point_size = std::min( average_point_size, std::min(curr_path.path_point_size(), next_path.path_point_size())); double lateral_distance_sum = 0.0; for (int i = 0; i < average_point_size; ++i) { lateral_distance_sum += fabs(curr_path.path_point(i).y() - next_path.path_point(i).y()); } double width = lateral_distance_sum / static_cast(average_point_size) / 2.0; width = common::math::Clamp(width, config_.min_lane_half_width(), config_.max_lane_half_width()); auto &curr_right_width = std::get<2>(*iter); auto &next_left_width = std::get<1>(*next_iter); if (std::get<0>(*iter) == std::get<0>(current_navi_path_tuple_)) { next_left_width = 2.0 * width - curr_right_width; } else { next_left_width = width; curr_right_width = width; } }

总结如果权重是动态变化的还请大家评论纠正我
感觉apollo的车道线也就是高精地图和感知通过权重进行融合没有很特别很精妙的算法如有不对之处还请指教