Apollo planning之PathAssessmentDecider

1 路径评估决策概览

路径评估决策是规划模块的task，属于task中的decider类别。依据原先设计好的规则，对各种path进行assess，排序得到最优的路径，并在规划路径上的采样点添加标签作为路径筛选的依据，并为速度规划提供限制


//输入
Status PathAssessmentDecider::Process(Frame* const frame, ReferenceLineInfo* const reference_line_info)
//输出最优路径并保存在reference_line_info

2 路径评估决策具体流程

2.1 路径重复使用


  ... ...
  // 如果路径重复使用则跳过
  if (FLAGS_enable_skip_path_tasks && reference_line_info->path_reusable()) {
    return Status::OK();
  ... ...

2.2 去掉无效路径


  ... ...
  // 1. 删掉无效路径.
  std::vector valid_path_data;
  for (const auto& curr_path_data : candidate_path_data) {
    // RecordDebugInfo(curr_path_data, curr_path_data.path_label(),
    //                 reference_line_info);
    if (curr_path_data.path_label().find("fallback") != std::string::npos) {
      if (IsValidFallbackPath(*reference_line_info, curr_path_data)) {
        valid_path_data.push_back(curr_path_data);
      }
    } else {
      if (IsValidRegularPath(*reference_line_info, curr_path_data)) {
        valid_path_data.push_back(curr_path_data);
      }
    }
  }
  const auto& end_time1 = std::chrono::system_clock::now();
  std::chrono::duration<double> diff = end_time1 - end_time0;
  ADEBUG << "Time for path validity checking: " << diff.count() * 1000
         << " msec.";
  ... ...

其中fallback的无效路径是偏离参考线、偏离道路的路径。regular的无效路径是偏离参考线、偏离道路，发生碰撞，停在相邻的逆向车道的路径

2.3 分析并加入重要信息，提供给速度决策器


  ... ...
  // 2. 分析并加入重要信息给speed决策
  size_t cnt = 0;
  const Obstacle* blocking_obstacle_on_selflane = nullptr;
  for (size_t i = 0; i != valid_path_data.size(); ++i) {
    auto& curr_path_data = valid_path_data[i];
    if (curr_path_data.path_label().find("fallback") != std::string::npos) {
      // remove empty path_data.
      if (!curr_path_data.Empty()) {
        if (cnt != i) {
          valid_path_data[cnt] = curr_path_data;
        }
        ++cnt;
      }
      continue;
    }
    SetPathInfo(*reference_line_info, &curr_path_data);
    // 修剪所有的借道路径，使其能够以in-lane结尾
    if (curr_path_data.path_label().find("pullover") == std::string::npos) {
      TrimTailingOutLanePoints(&curr_path_data);
    }
 
    // 找到 blocking_obstacle_on_selflane, 为下一步选择车道做准备
    if (curr_path_data.path_label().find("self") != std::string::npos) {
      const auto blocking_obstacle_id = curr_path_data.blocking_obstacle_id();
      blocking_obstacle_on_selflane =
          reference_line_info->path_decision()->Find(blocking_obstacle_id);
    }
 
    // 删掉空路径
    if (!curr_path_data.Empty()) {
      if (cnt != i) {
        valid_path_data[cnt] = curr_path_data;
      }
      ++cnt;
    }
 
    // RecordDebugInfo(curr_path_data, curr_path_data.path_label(),
    //                 reference_line_info);
    ADEBUG << "For " << curr_path_data.path_label() << ", "
           << "path length = " << curr_path_data.frenet_frame_path().size();
  }
  valid_path_data.resize(cnt);
  // 如果没有有效路径，退出
  if (valid_path_data.empty()) {
    const std::string msg = "Neither regular nor fallback path is valid.";
    AERROR << msg;
    return Status(ErrorCode::PLANNING_ERROR, msg);
  }
  ADEBUG << "There are " << valid_path_data.size() << " valid path data.";
  const auto& end_time2 = std::chrono::system_clock::now();
  diff = end_time2 - end_time1;
  ADEBUG << "Time for path info labeling: " << diff.count() * 1000 << " msec.";
  ... ...

2.4 排序选出最优的轨迹


  ... ...
  // 3. Pick the optimal path.
  std::sort(valid_path_data.begin(), valid_path_data.end(),
            std::bind(ComparePathData, std::placeholders::_1,
                      std::placeholders::_2, blocking_obstacle_on_selflane));
 
  ADEBUG << "Using '" << valid_path_data.front().path_label()
         << "' path out of " << valid_path_data.size() << " path(s)";
  if (valid_path_data.front().path_label().find("fallback") !=
      std::string::npos) {
    FLAGS_static_obstacle_nudge_l_buffer = 0.8;
  }
  *(reference_line_info->mutable_path_data()) = valid_path_data.front();
  reference_line_info->SetBlockingObstacle(
      valid_path_data.front().blocking_obstacle_id());
  const auto& end_time3 = std::chrono::system_clock::now();
  diff = end_time3 - end_time2;
  ADEBUG << "Time for optimal path selection: " << diff.count() * 1000
         << " msec.";
 
  reference_line_info->SetCandidatePathData(std::move(valid_path_data));
  ... ...

排序算法的流程具体如下：

ComparePathData

路径排序：（道路评估的优劣通过排序获得）

1.空的路径永远排在后面
2.regular path 优先于 fallback path，regular path 的补集包含 fallback path 的补集
3.如果self-lane有一个存在，选择self-lane。如果都存在，选择较长的。如果长度接近，选择self-lane。如果self-lane都不存在，选择较长的路径
4.如果路径长度接近，且都要借道:
- (1) 都要借逆向车道，选择距离短的
- (2) 针对具有两个借道方向的情况:
  - 有障碍物，选择合适的方向，左或右借道
  - 无障碍物，根据adc的位置选择借道方向
- (3) 路径长度相同，相邻车道都是前向的，选择较早返回自车道的路径
- (4) 如果路径长度相同，前向借道，返回自车道时间相同，选择从左侧借道的路径
5.最后如果两条路径相同，则 lhs is not < rhl排序之后：选择最优路径，即第一个路径

2.5 更新必要信息


  // 4. Update necessary info for lane-borrow decider's future uses.
  // Update front static obstacle's info.
  auto* mutable_path_decider_status = injector_->planning_context()
                                          ->mutable_planning_status()
                                          ->mutable_path_decider();
  if (reference_line_info->GetBlockingObstacle() != nullptr) {
    int front_static_obstacle_cycle_counter =
        mutable_path_decider_status->front_static_obstacle_cycle_counter();
    mutable_path_decider_status->set_front_static_obstacle_cycle_counter(
        std::max(front_static_obstacle_cycle_counter, 0));
    mutable_path_decider_status->set_front_static_obstacle_cycle_counter(
        std::min(front_static_obstacle_cycle_counter + 1, 10));
    mutable_path_decider_status->set_front_static_obstacle_id(
        reference_line_info->GetBlockingObstacle()->Id());
  } else {
    int front_static_obstacle_cycle_counter =
        mutable_path_decider_status->front_static_obstacle_cycle_counter();
    mutable_path_decider_status->set_front_static_obstacle_cycle_counter(
        std::min(front_static_obstacle_cycle_counter, 0));
    mutable_path_decider_status->set_front_static_obstacle_cycle_counter(
        std::max(front_static_obstacle_cycle_counter - 1, -10));
  }
 
  // Update self-lane usage info.
  if (reference_line_info->path_data().path_label().find("self") !=
      std::string::npos) {
    // && std::get<1>(reference_line_info->path_data()
    //                 .path_point_decision_guide()
    //                 .front()) == PathData::PathPointType::IN_LANE)
    int able_to_use_self_lane_counter =
        mutable_path_decider_status->able_to_use_self_lane_counter();
 
    if (able_to_use_self_lane_counter < 0) {
      able_to_use_self_lane_counter = 0;
    }
    mutable_path_decider_status->set_able_to_use_self_lane_counter(
        std::min(able_to_use_self_lane_counter + 1, 10));
  } else {
    mutable_path_decider_status->set_able_to_use_self_lane_counter(0);
  }
 
  // Update side-pass direction.
  if (mutable_path_decider_status->is_in_path_lane_borrow_scenario()) {
    bool left_borrow = false;
    bool right_borrow = false;
    const auto& path_decider_status =
        injector_->planning_context()->planning_status().path_decider();
    for (const auto& lane_borrow_direction :
         path_decider_status.decided_side_pass_direction()) {
      if (lane_borrow_direction == PathDeciderStatus::LEFT_BORROW &&
          reference_line_info->path_data().path_label().find("left") !=
              std::string::npos) {
        left_borrow = true;
      }
      if (lane_borrow_direction == PathDeciderStatus::RIGHT_BORROW &&
          reference_line_info->path_data().path_label().find("right") !=
              std::string::npos) {
        right_borrow = true;
      }
    }
 
    mutable_path_decider_status->clear_decided_side_pass_direction();
    if (right_borrow) {
      mutable_path_decider_status->add_decided_side_pass_direction(
          PathDeciderStatus::RIGHT_BORROW);
    }
    if (left_borrow) {
      mutable_path_decider_status->add_decided_side_pass_direction(
          PathDeciderStatus::LEFT_BORROW);
    }
  }
  const auto& end_time4 = std::chrono::system_clock::now();
  diff = end_time4 - end_time3;
  ADEBUG << "Time for FSM state updating: " << diff.count() * 1000 << " msec.";
 
  // Plot the path in simulator for debug purpose.
  RecordDebugInfo(reference_line_info->path_data(), "Planning PathData",
                  reference_line_info);
  return Status::OK();

更新front static obstacle's info，即前方静态障碍物的信息
更新self-lane usage info，即自车道使用信息
更新side-pass direction，根据PathDeciderStatus是left_borrow或right_borrow，即判断是从左侧借道，还是从右侧借道

本文参考路径评估决策 — Apollo Auto 0.0.1 文档 (daobook.github.io)

如有侵权，请联系删除

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原文地址：https://blog.csdn.net/weixin_65089713/article/details/126334055