前言

在Apollo星火计划学习笔记——Apollo路径规划算法原理与实践与【Apollo学习笔记】——Planning模块讲到……Stage::Process的PlanOnReferenceLine函数会依次调用task_list中的TASK，本文将会继续以LaneFollow为例依次介绍其中的TASK部分究竟做了哪些工作。由于个人能力所限，文章可能有纰漏的地方，还请批评斧正。

在modules/planning/conf/scenario/lane_follow_config.pb.txt配置文件中，我们可以看到LaneFollow所需要执行的所有task。

stage_config: {
  stage_type: LANE_FOLLOW_DEFAULT_STAGE
  enabled: true
  task_type: LANE_CHANGE_DECIDER
  task_type: PATH_REUSE_DECIDER
  task_type: PATH_LANE_BORROW_DECIDER
  task_type: PATH_BOUNDS_DECIDER
  task_type: PIECEWISE_JERK_PATH_OPTIMIZER
  task_type: PATH_ASSESSMENT_DECIDER
  task_type: PATH_DECIDER
  task_type: RULE_BASED_STOP_DECIDER
  task_type: SPEED_BOUNDS_PRIORI_DECIDER
  task_type: SPEED_HEURISTIC_OPTIMIZER
  task_type: SPEED_DECIDER
  task_type: SPEED_BOUNDS_FINAL_DECIDER
  task_type: PIECEWISE_JERK_SPEED_OPTIMIZER
  # task_type: PIECEWISE_JERK_NONLINEAR_SPEED_OPTIMIZER
  task_type: RSS_DECIDER

本文将继续介绍LaneFollow的第9个TASK——SPEED_BOUNDS_PRIORI_DECIDER

SPEED_BOUNDS_PRIORI_DECIDER功能简介

产生速度可行驶边界

所形成的区域是非凸的，不能用之前凸优化的方法去做，需要用动态规划的方法去做。

SPEED_BOUNDS_PRIORI_DECIDER相关配置

modules/planning/proto/task_config.proto

// SpeedBoundsDeciderConfig

message SpeedBoundsDeciderConfig {
  optional double total_time = 1 [default = 7.0];
  optional double boundary_buffer = 2 [default = 0.1];
  optional double max_centric_acceleration_limit = 3 [default = 2.0];
  optional double minimal_kappa = 4 [default = 0.00001];
  optional double point_extension = 5 [default = 1.0];
  optional double lowest_speed = 6 [default = 2.5];
  optional double collision_safety_range = 7 [default = 1.0];
  optional double static_obs_nudge_speed_ratio = 8;
  optional double dynamic_obs_nudge_speed_ratio = 9;
}

modules/planning/conf/planning_config.pb.txt

default_task_config: {
  task_type: ST_BOUNDS_DECIDER
  st_bounds_decider_config {
    total_time: 7.0
  }
}

SPEED_BOUNDS_PRIORI_DECIDER流程

通过modules/planning/tasks/task_factory.cc，我们可以看到SPEED_BOUNDS_PRIORI_DECIDER按以下方式进行注册：

  task_factory_.Register(
      TaskConfig::SPEED_BOUNDS_PRIORI_DECIDER,
      [](const TaskConfig& config,
         const std::shared_ptr<DependencyInjector>& injector) -> Task* {
        return new SpeedBoundsDecider(config, injector);
      });

也据此可知，SPEED_BOUNDS_PRIORI_DECIDER代码实现的部分在modules/planning/tasks/deciders/speed_bounds_decider/speed_bounds_decider.cc中。

Speed bounds decider 主要完成以下任务：

1. 将障碍物映射到ST图中
2. 创建速度限制
3. 获取路径长度以及时间长度作为ST边界

SpeedBoundsDecider是一个继承自Decider的派生类。当task_list中运行task::Execute()时，SpeedBoundsDecider中的Process部分开始运行。

• 输入：frame 和reference_line_info。通过计算PathData/ReferenceLine/PathDecision/PlanningStartPoint等等信息，来得到ST_Graph。

• Process：

  • 将障碍物映射到ST图中。(boundary_mapper.ComputeSTBoundary(path_decision).code() == ErrorCode::PLANNING_ERROR) {}此处将会遍历所有的障碍物去生成ST graph。当有纵向决策产生时，会对边界进行细微调整。再此之后，所有的障碍物的st_boundary会送入一个boundaries vector之中进行保存。
  • 创建速度限制。if (!speed_limit_decider.GetSpeedLimits(path_decision->obstacles(), &speed_limit).ok())此处会遍历每一个离散的路径点并且找到其速度限制。在每一个循环中，基本速度会取决于map/path_curvature/nudge obstacles等因素。对于nudge obstacles，需要找到最近的障碍物。
  • 获取路径长度以及时间长度作为搜索边界。时间长度来自于配置文件total_time: 7.0（配置部分已有介绍）

• 输出：boundaries/speed_limit 会存储在reference_line_info的st_graph_data中。

Status SpeedBoundsDecider::Process(
    Frame *const frame, ReferenceLineInfo *const reference_line_info) {
  // retrieve data from frame and reference_line_info
  const PathData &path_data = reference_line_info->path_data();
  const TrajectoryPoint &init_point = frame->PlanningStartPoint();
  const ReferenceLine &reference_line = reference_line_info->reference_line();
  PathDecision *const path_decision = reference_line_info->path_decision();

  // 1. Map obstacles into st graph
  auto time1 = std::chrono::system_clock::now();
  // 构造一个STBoundary映射对象
  STBoundaryMapper boundary_mapper(
      speed_bounds_config_, reference_line, path_data,
      path_data.discretized_path().Length(), speed_bounds_config_.total_time(),
      injector_);
  // FLAGS_use_st_drivable_boundary: True to use st_drivable boundary in speed planning
  // default: false
  // 清除STBoundary
  if (!FLAGS_use_st_drivable_boundary) {
    path_decision->EraseStBoundaries();
  }
  // 将障碍物投影到ST Gragh上
  if (boundary_mapper.ComputeSTBoundary(path_decision).code() ==
      ErrorCode::PLANNING_ERROR) {
    const std::string msg = "Mapping obstacle failed.";
    AERROR << msg;
    return Status(ErrorCode::PLANNING_ERROR, msg);
  }
  auto time2 = std::chrono::system_clock::now();
  std::chrono::duration<double> diff = time2 - time1;
  ADEBUG << "Time for ST Boundary Mapping = " << diff.count() * 1000
         << " msec.";
  // 所有的障碍物的st_boundary送入到一个boundaries vector之中进行保存
  std::vector<const STBoundary *> boundaries;
  for (auto *obstacle : path_decision->obstacles().Items()) {
    const auto &id = obstacle->Id();
    const auto &st_boundary = obstacle->path_st_boundary();
    if (!st_boundary.IsEmpty()) {
      if (st_boundary.boundary_type() == STBoundary::BoundaryType::KEEP_CLEAR) {
        path_decision->Find(id)->SetBlockingObstacle(false);
      } else {
        path_decision->Find(id)->SetBlockingObstacle(true);
      }
      boundaries.push_back(&st_boundary);
    }
  }

  const double min_s_on_st_boundaries = SetSpeedFallbackDistance(path_decision);

  // 2. Create speed limit along path
  SpeedLimitDecider speed_limit_decider(speed_bounds_config_, reference_line,
                                        path_data);

  SpeedLimit speed_limit;
  if (!speed_limit_decider
           .GetSpeedLimits(path_decision->obstacles(), &speed_limit)
           .ok()) {
    const std::string msg = "Getting speed limits failed!";
    AERROR << msg;
    return Status(ErrorCode::PLANNING_ERROR, msg);
  }

  // 3. Get path_length as s axis search bound in st graph
  const double path_data_length = path_data.discretized_path().Length();

  // 4. Get time duration as t axis search bound in st graph
  const double total_time_by_conf = speed_bounds_config_.total_time();

  // Load generated st graph data back to frame
  StGraphData *st_graph_data = reference_line_info_->mutable_st_graph_data();

  // Add a st_graph debug info and save the pointer to st_graph_data for
  // optimizer logging
  auto *debug = reference_line_info_->mutable_debug();
  STGraphDebug *st_graph_debug = debug->mutable_planning_data()->add_st_graph();

  st_graph_data->LoadData(boundaries, min_s_on_st_boundaries, init_point,
                          speed_limit, reference_line_info->GetCruiseSpeed(),
                          path_data_length, total_time_by_conf, st_graph_debug);

  // Create and record st_graph debug info
  RecordSTGraphDebug(*st_graph_data, st_graph_debug);

  return Status::OK();
}

将障碍物映射到ST图中

由Process部分代码可知，(boundary_mapper.ComputeSTBoundary(path_decision).code() == ErrorCode::PLANNING_ERROR) {}此处是函数的入口。

该部分的流程示意图如下图所示：

ComputeSTBoundary(PathDecision* path_decision)

ComputeSTBoundary将会遍历所有的障碍物去生成ST graph。当有纵向决策产生时，会对边界进行细微调整。再此之后，所有的障碍物的st_boundary会送入一个boundaries vector之中进行保存。

Status STBoundaryMapper::ComputeSTBoundary(PathDecision* path_decision) const {
  // Sanity checks.
  CHECK_GT(planning_max_time_, 0.0);
  if (path_data_.discretized_path().size() < 2) {
    AERROR << "Fail to get params because of too few path points. path points "
              "size: "
           << path_data_.discretized_path().size() << ".";
    return Status(ErrorCode::PLANNING_ERROR,
                  "Fail to get params because of too few path points");
  }

  // Go through every obstacle.
  Obstacle* stop_obstacle = nullptr;
  ObjectDecisionType stop_decision;
  double min_stop_s = std::numeric_limits<double>::max();
  for (const auto* ptr_obstacle_item : path_decision->obstacles().Items()) {
    Obstacle* ptr_obstacle = path_decision->Find(ptr_obstacle_item->Id());
    ACHECK(ptr_obstacle != nullptr);

    // If no longitudinal decision has been made, then plot it onto ST-graph.
    if (!ptr_obstacle->HasLongitudinalDecision()) {
      ComputeSTBoundary(ptr_obstacle);
      continue;
    }

    // If there is a longitudinal decision, then fine-tune boundary.
    const auto& decision = ptr_obstacle->LongitudinalDecision();
    if (decision.has_stop()) {
      // 1. Store the closest stop fence info.
      // TODO(all): store ref. s value in stop decision; refine the code then.
      common::SLPoint stop_sl_point;
      reference_line_.XYToSL(decision.stop().stop_point(), &stop_sl_point);
      const double stop_s = stop_sl_point.s();

      if (stop_s < min_stop_s) {
        stop_obstacle = ptr_obstacle;
        min_stop_s = stop_s;
        stop_decision = decision;
      }
    } else if (decision.has_follow() || decision.has_overtake() ||
               decision.has_yield()) {
      // 2. Depending on the longitudinal overtake/yield decision,
      //    fine-tune the upper/lower st-boundary of related obstacles.
      ComputeSTBoundaryWithDecision(ptr_obstacle, decision);
    } else if (!decision.has_ignore()) {
      // 3. Ignore those unrelated obstacles.
      AWARN << "No mapping for decision: " << decision.DebugString();
    }
  }
  if (stop_obstacle) {
    bool success = MapStopDecision(stop_obstacle, stop_decision);
    if (!success) {
      const std::string msg = "Fail to MapStopDecision.";
      AERROR << msg;
      return Status(ErrorCode::PLANNING_ERROR, msg);
    }
  }

  return Status::OK();
}

ComputeSTBoundary(Obstacle* obstacle)

调用GetOverlapBoundaryPoints来获取给定障碍物的上下点，然后在此基础上制定STBoundary。它还根据以前记录的决策标记边界类型。

void STBoundaryMapper::ComputeSTBoundary(Obstacle* obstacle) const {
  if (FLAGS_use_st_drivable_boundary) {
    return;
  }
  std::vector<STPoint> lower_points;
  std::vector<STPoint> upper_points;
  // Map the given obstacle onto the ST-Graph.
  if (!GetOverlapBoundaryPoints(path_data_.discretized_path(), *obstacle,
                                &upper_points, &lower_points)) {
    return;
  }
  // 构建障碍物的boundary
  auto boundary = STBoundary::CreateInstance(lower_points, upper_points);
  boundary.set_id(obstacle->Id());

  // TODO(all): potential bug here.
  const auto& prev_st_boundary = obstacle->path_st_boundary();
  const auto& ref_line_st_boundary = obstacle->reference_line_st_boundary();
  if (!prev_st_boundary.IsEmpty()) {
    boundary.SetBoundaryType(prev_st_boundary.boundary_type());
  } else if (!ref_line_st_boundary.IsEmpty()) {
    boundary.SetBoundaryType(ref_line_st_boundary.boundary_type());
  }

  obstacle->set_path_st_boundary(boundary);
}

GetOverlapBoundaryPoints

将给定的障碍物映射到ST图中。

// Map the given obstacle onto the ST-Graph. 
// The boundary is represented as upper and lower points for every s of interests.
// Note that upper_points.size() = lower_points.size()
bool STBoundaryMapper::GetOverlapBoundaryPoints(
    const std::vector<PathPoint>& path_points, const Obstacle& obstacle,
    std::vector<STPoint>* upper_points,
    std::vector<STPoint>* lower_points) const {
  // Sanity checks.
  DCHECK(upper_points->empty());
  DCHECK(lower_points->empty());
  if (path_points.empty()) {
    AERROR << "No points in path_data_.discretized_path().";
    return false;
  }

  const auto* planning_status = injector_->planning_context()
                                    ->mutable_planning_status()
                                    ->mutable_change_lane();
  // lane_change_obstacle_nudge_l_buffer: minimum l-distance to nudge when changing lane (meters);0.3
  // nonstatic_obstacle_nudge_l_buffer: minimum l-distance to nudge a non-static obstacle (meters);0.4
  double l_buffer =
      planning_status->status() == ChangeLaneStatus::IN_CHANGE_LANE
          ? FLAGS_lane_change_obstacle_nudge_l_buffer
          : FLAGS_nonstatic_obstacle_nudge_l_buffer;

  // Draw the given obstacle on the ST-graph.
  const auto& trajectory = obstacle.Trajectory();
  if (trajectory.trajectory_point().empty()) {
    // For those with no predicted trajectories, just map the obstacle's
    // current position to ST-graph and always assume it's static.
    if (!obstacle.IsStatic()) {
      AWARN << "Non-static obstacle[" << obstacle.Id()
            << "] has NO prediction trajectory."
            << obstacle.Perception().ShortDebugString();
    }
    // 遍历离散路径点
    for (const auto& curr_point_on_path : path_points) {
      // planning_max_distance_ = path_data.discretized_path().Length()
      // 若当前点超过了规划最大距离，退出
      if (curr_point_on_path.s() > planning_max_distance_) {
        break;
      }
      // 获取障碍物的boundingbox
      const Box2d& obs_box = obstacle.PerceptionBoundingBox();
      if (CheckOverlap(curr_point_on_path, obs_box, l_buffer)) {
        // If there is overlapping, then plot it on ST-graph.
        const double backward_distance = -vehicle_param_.front_edge_to_center();
        const double forward_distance = obs_box.length();
        double low_s =
            std::fmax(0.0, curr_point_on_path.s() + backward_distance);
        double high_s = std::fmin(planning_max_distance_,
                                  curr_point_on_path.s() + forward_distance);
        // It is an unrotated rectangle appearing on the ST-graph.
        // 静止的障碍物在ST图中就是一个矩形
        // TODO(jiacheng): reconsider the backward_distance, it might be
        // unnecessary, but forward_distance is indeed meaningful though.
        lower_points->emplace_back(low_s, 0.0);
        lower_points->emplace_back(low_s, planning_max_time_);
        upper_points->emplace_back(high_s, 0.0);
        upper_points->emplace_back(high_s, planning_max_time_);
        break;
      }
    }
  } else {
    // For those with predicted trajectories (moving obstacles):
    // 1. Subsample to reduce computation time.
    const int default_num_point = 50;
    DiscretizedPath discretized_path;
    if (path_points.size() > 2 * default_num_point) {
      const auto ratio = path_points.size() / default_num_point;
      std::vector<PathPoint> sampled_path_points;
      for (size_t i = 0; i < path_points.size(); ++i) {
        if (i % ratio == 0) {
          sampled_path_points.push_back(path_points[i]);
        }
      }
      discretized_path = DiscretizedPath(std::move(sampled_path_points));
    } else {
      discretized_path = DiscretizedPath(path_points);
    }
    // 2. Go through every point of the predicted obstacle trajectory.
    for (int i = 0; i < trajectory.trajectory_point_size(); ++i) {
      const auto& trajectory_point = trajectory.trajectory_point(i);
      // 得到障碍物在轨迹点处的boundingbox
      const Box2d obs_box = obstacle.GetBoundingBox(trajectory_point);
      // 得到障碍物在轨迹点处的相对时间
      double trajectory_point_time = trajectory_point.relative_time();
      static constexpr double kNegtiveTimeThreshold = -1.0;
      // 跳过小于阈值时间的轨迹点
      if (trajectory_point_time < kNegtiveTimeThreshold) {
        continue;
      }
      // 步长
      const double step_length = vehicle_param_.front_edge_to_center();
      // FLAGS_max_trajectory_len: (unit: meter) max possible trajectory length. 1000.0
      auto path_len =
          std::min(FLAGS_max_trajectory_len, discretized_path.Length());
      // Go through every point of the ADC's path.
      for (double path_s = 0.0; path_s < path_len; path_s += step_length) {
        // 估计当前车辆的位置
        const auto curr_adc_path_point =
            discretized_path.Evaluate(path_s + discretized_path.front().s());
        if (CheckOverlap(curr_adc_path_point, obs_box, l_buffer)) {
          // Found overlap, start searching with higher resolution
          const double backward_distance = -step_length;
          const double forward_distance = vehicle_param_.length() +
                                          vehicle_param_.width() +
                                          obs_box.length() + obs_box.width();
          const double default_min_step = 0.1;  // in meters
          const double fine_tuning_step_length = std::fmin(
              default_min_step, discretized_path.Length() / default_num_point);

          bool find_low = false;
          bool find_high = false;
          double low_s = std::fmax(0.0, path_s + backward_distance);
          double high_s =
              std::fmin(discretized_path.Length(), path_s + forward_distance);

          // Keep shrinking by the resolution bidirectionally until finally
          // locating the tight upper and lower bounds.
          while (low_s < high_s) {
            if (find_low && find_high) {
              break;
            }
            if (!find_low) {
              const auto& point_low = discretized_path.Evaluate(
                  low_s + discretized_path.front().s());
              if (!CheckOverlap(point_low, obs_box, l_buffer)) {
                low_s += fine_tuning_step_length;
              } else {
                find_low = true;
              }
            }
            if (!find_high) {
              const auto& point_high = discretized_path.Evaluate(
                  high_s + discretized_path.front().s());
              if (!CheckOverlap(point_high, obs_box, l_buffer)) {
                high_s -= fine_tuning_step_length;
              } else {
                find_high = true;
              }
            }
          }
          if (find_high && find_low) {
            lower_points->emplace_back(
                low_s - speed_bounds_config_.point_extension(),
                trajectory_point_time);
            upper_points->emplace_back(
                high_s + speed_bounds_config_.point_extension(),
                trajectory_point_time);
          }
          break;
        }
      }
    }
  }

  // Sanity checks and return.
  DCHECK_EQ(lower_points->size(), upper_points->size());
  return (lower_points->size() > 1 && upper_points->size() > 1);
}

ComputeSTBoundaryWithDecision

对于产生纵向决策的障碍物产生的ST boundary进行调整。

// Fine-tune the boundary for yielding or overtaking obstacles. 
// Increase boundary on the s-dimension or set the boundary type, etc., when necessary.
void STBoundaryMapper::ComputeSTBoundaryWithDecision(
    Obstacle* obstacle, const ObjectDecisionType& decision) const {
  DCHECK(decision.has_follow() || decision.has_yield() ||
         decision.has_overtake())
      << "decision is " << decision.DebugString()
      << ", but it must be follow or yield or overtake.";

  std::vector<STPoint> lower_points;
  std::vector<STPoint> upper_points;

  if (FLAGS_use_st_drivable_boundary &&
      obstacle->is_path_st_boundary_initialized()) {
    const auto& path_st_boundary = obstacle->path_st_boundary();
    lower_points = path_st_boundary.lower_points();
    upper_points = path_st_boundary.upper_points();
  } else {
    if (!GetOverlapBoundaryPoints(path_data_.discretized_path(), *obstacle,
                                  &upper_points, &lower_points)) {
      return;
    }
  }

  auto boundary = STBoundary::CreateInstance(lower_points, upper_points);

  // get characteristic_length and boundary_type.
  STBoundary::BoundaryType b_type = STBoundary::BoundaryType::UNKNOWN;
  double characteristic_length = 0.0;
  if (decision.has_follow()) {
    characteristic_length = std::fabs(decision.follow().distance_s());
    b_type = STBoundary::BoundaryType::FOLLOW;
  } else if (decision.has_yield()) {
    characteristic_length = std::fabs(decision.yield().distance_s());
    boundary = STBoundary::CreateInstance(lower_points, upper_points)
                   .ExpandByS(characteristic_length);
    b_type = STBoundary::BoundaryType::YIELD;
  } else if (decision.has_overtake()) {
    characteristic_length = std::fabs(decision.overtake().distance_s());
    b_type = STBoundary::BoundaryType::OVERTAKE;
  } else {
    DCHECK(false) << "Obj decision should be either yield or overtake: "
                  << decision.DebugString();
  }
  boundary.SetBoundaryType(b_type);
  boundary.set_id(obstacle->Id());
  boundary.SetCharacteristicLength(characteristic_length);
  obstacle->set_path_st_boundary(boundary);
}

SetSpeedFallbackDistance

找到障碍物路径上最低的 s 值，该 s 值将用作速度回退的距离。

double SpeedBoundsDecider::SetSpeedFallbackDistance(
    PathDecision *const path_decision) {
  // Set min_s_on_st_boundaries to guide speed fallback.
  static constexpr double kEpsilon = 1.0e-6;
  double min_s_non_reverse = std::numeric_limits<double>::infinity();
  double min_s_reverse = std::numeric_limits<double>::infinity();
  // 遍历障碍物
  for (auto *obstacle : path_decision->obstacles().Items()) {
    const auto &st_boundary = obstacle->path_st_boundary();
    // 障碍物ST边界为空，跳过
    if (st_boundary.IsEmpty()) {
      continue;
    }
    // 获取st边界底部左侧点和右侧点的s值，并选择较小的值作为最低的s值
    const auto left_bottom_point_s = st_boundary.bottom_left_point().s();
    const auto right_bottom_point_s = st_boundary.bottom_right_point().s();
    const auto lowest_s = std::min(left_bottom_point_s, right_bottom_point_s);
    // 如果左侧点的 s 值减去右侧点的 s 值大于 kEpsilon（即左侧点在右侧点之后），则说明这是一个反向行驶的边界
    if (left_bottom_point_s - right_bottom_point_s > kEpsilon) {
      // 更新 min_s_reverse，将其设置为最低的 s 值
      if (min_s_reverse > lowest_s) {
        min_s_reverse = lowest_s;
      }
    } else if (min_s_non_reverse > lowest_s) {
      // 更新 min_s_non_reverse，将其设置为最低的 s 值。
      min_s_non_reverse = lowest_s;
    }
  }

  min_s_reverse = std::max(min_s_reverse, 0.0);
  min_s_non_reverse = std::max(min_s_non_reverse, 0.0);

  return min_s_non_reverse > min_s_reverse ? 0.0 : min_s_non_reverse;
}

创建速度限制

SpeedLimits来源于三个部分：

1. 来自于地图的速度限制
2. 来自于道路曲率的速度限制
3. 来自于Nudge障碍物的速度限制

该部分流程图如下所示：

GetSpeedLimits

Status SpeedLimitDecider::GetSpeedLimits(
    const IndexedList<std::string, Obstacle>& obstacles,
    SpeedLimit* const speed_limit_data) const {
  CHECK_NOTNULL(speed_limit_data);

  const auto& discretized_path = path_data_.discretized_path();
  const auto& frenet_path = path_data_.frenet_frame_path();
  // 遍历离散路径点
  for (uint32_t i = 0; i < discretized_path.size(); ++i) {
    const double path_s = discretized_path.at(i).s();
    const double reference_line_s = frenet_path.at(i).s();
    if (reference_line_s > reference_line_.Length()) {
      AWARN << "path w.r.t. reference line at [" << reference_line_s
            << "] is LARGER than reference_line_ length ["
            << reference_line_.Length() << "]. Please debug before proceeding.";
      break;
    }

    // (1) speed limit from map
    double speed_limit_from_reference_line =
        reference_line_.GetSpeedLimitFromS(reference_line_s);

    // (2) speed limit from path curvature
    //  -- 2.1: limit by centripetal force (acceleration)
    const double speed_limit_from_centripetal_acc =
    // max_centric_acceleration_limit default = 2.0
        std::sqrt(speed_bounds_config_.max_centric_acceleration_limit() /
                  std::fmax(std::fabs(discretized_path.at(i).kappa()),
                            speed_bounds_config_.minimal_kappa()));

    // (3) speed limit from nudge obstacles
    // TODO(all): in future, expand the speed limit not only to obstacles with
    // nudge decisions.
    double speed_limit_from_nearby_obstacles =
        std::numeric_limits<double>::max();
    const double collision_safety_range =
        speed_bounds_config_.collision_safety_range();// default = 1.0
    // 遍历障碍物
    for (const auto* ptr_obstacle : obstacles.Items()) {
      // 跳过虚拟的障碍物
      if (ptr_obstacle->IsVirtual()) {
        continue;
      }
      // 障碍物没有横向Nudge决策，跳过
      if (!ptr_obstacle->LateralDecision().has_nudge()) {
        continue;
      }

      /* ref line:
       * -------------------------------
       *    start_s   end_s
       * ------|  adc   |---------------
       * ------------|  obstacle |------
       */

      // TODO(all): potential problem here;
      // frenet and cartesian coordinates are mixed.
      const double vehicle_front_s =
          reference_line_s + vehicle_param_.front_edge_to_center();
      const double vehicle_back_s =
          reference_line_s - vehicle_param_.back_edge_to_center();
      const double obstacle_front_s =
          ptr_obstacle->PerceptionSLBoundary().end_s();
      const double obstacle_back_s =
          ptr_obstacle->PerceptionSLBoundary().start_s();
      // 若车辆与障碍物在s方向上没有发生重合,跳过
      if (vehicle_front_s < obstacle_back_s ||
          vehicle_back_s > obstacle_front_s) {
        continue;
      }

      const auto& nudge_decision = ptr_obstacle->LateralDecision().nudge();

      // Please notice the differences between adc_l and frenet_point_l
      const double frenet_point_l = frenet_path.at(i).l();

      // obstacle is on the right of ego vehicle (at path point i)
      bool is_close_on_left =
          (nudge_decision.type() == ObjectNudge::LEFT_NUDGE) &&
          (frenet_point_l - vehicle_param_.right_edge_to_center() -
               collision_safety_range <
           ptr_obstacle->PerceptionSLBoundary().end_l());

      // obstacle is on the left of ego vehicle (at path point i)
      bool is_close_on_right =
          (nudge_decision.type() == ObjectNudge::RIGHT_NUDGE) &&
          (ptr_obstacle->PerceptionSLBoundary().start_l() -
               collision_safety_range <
           frenet_point_l + vehicle_param_.left_edge_to_center());

      // TODO(all): dynamic obstacles do not have nudge decision
      if (is_close_on_left || is_close_on_right) {
        double nudge_speed_ratio = 1.0;
        // 静态障碍物 x 0.6
        if (ptr_obstacle->IsStatic()) {
          nudge_speed_ratio =
              speed_bounds_config_.static_obs_nudge_speed_ratio(); // static_obs_nudge_speed_ratio: 0.6
        } else {
          // 动态障碍物 x 0.8
          nudge_speed_ratio =
              speed_bounds_config_.dynamic_obs_nudge_speed_ratio(); // dynamic_obs_nudge_speed_ratio: 0.8
        }
        speed_limit_from_nearby_obstacles =
            nudge_speed_ratio * speed_limit_from_reference_line;
        break;
      }
    }

    double curr_speed_limit = 0.0;
    // FLAGS_enable_nudge_slowdown: True to slow down when nudge obstacles
    if (FLAGS_enable_nudge_slowdown) {
      curr_speed_limit =
          std::fmax(speed_bounds_config_.lowest_speed(),   // lowest_speed:2.5
                    std::min({speed_limit_from_reference_line,
                              speed_limit_from_centripetal_acc,
                              speed_limit_from_nearby_obstacles}));
    } else {
      curr_speed_limit =
          std::fmax(speed_bounds_config_.lowest_speed(),
                    std::min({speed_limit_from_reference_line,
                              speed_limit_from_centripetal_acc}));
    }
    speed_limit_data->AppendSpeedLimit(path_s, curr_speed_limit);
  }
  return Status::OK();
}

设置标志位（is_close_on_left、is_close_on_right）部分的示意图如下：

GetSpeedLimitFromS

double ReferenceLine::GetSpeedLimitFromS(const double s) const {
  // 对于速度限制列表speed_limit_中已经有的部分，直接返回相应的值。
  for (const auto& speed_limit : speed_limit_) {
    if (s >= speed_limit.start_s && s <= speed_limit.end_s) {
      return speed_limit.speed_limit;
    }
  }
  const auto& map_path_point = GetReferencePoint(s);
  // FLAGS_planning_upper_speed_limit: Maximum speed (m/s) in planning;31.3
  double speed_limit = FLAGS_planning_upper_speed_limit;
  bool speed_limit_found = false;
  // 根据lane_waypoint道路的情况进行速度限制
  for (const auto& lane_waypoint : map_path_point.lane_waypoints()) {
    if (lane_waypoint.lane == nullptr) {
      AWARN << "lane_waypoint.lane is nullptr.";
      continue;
    }
    speed_limit_found = true;
    speed_limit =
        std::fmin(lane_waypoint.lane->lane().speed_limit(), speed_limit);
  }
  // 若未找到lane_waypoint.lane，根据道路类型进行限制
  if (!speed_limit_found) {
    // use default speed limit based on road_type
    // FLAGS_default_city_road_speed_limit: default speed limit (m/s) for city road. 35 mph
    speed_limit = FLAGS_default_city_road_speed_limit;
    hdmap::Road::Type road_type = GetRoadType(s);
    if (road_type == hdmap::Road::HIGHWAY) {
      // FLAGS_default_highway_speed_limit: default speed limit (m/s) for highway. 65 mph
      speed_limit = FLAGS_default_highway_speed_limit;
    }
  }

  return speed_limit;
}

参考

[1] Planning Piecewise Jerk Nonlinear Speed Optimizer Introduction
[2] Apollo规划控制学习笔记
[3] 1.10 Speed Bounds Prior Decider