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开发者说丨Planning 模块源代码分析
规划（Planning）模块位于命名空间：apollo::planning，其作用在于构建无人车从起点到终点的局部行驶路径，具体而言，就是给定导航地图、导航路径、当前定位点、车辆状态、 周边目标的感知及预测信息，规划模块计算出可供控制模块执行的一条安全且舒适的行驶路径。
规划模块输出的路径是局部路径而非全局路径。举例，如无人车需从长沙智能驾驶研究院行驶至长沙高铁南站，首先需借助Routing模块输出全局导航路径，接下来才是规划模块基于全局导航路径进行一小段、一小段具体行驶路径的规划。
规划模块的作用是根据感知预测的结果，当前的车辆信息和路况规划出一条车辆能够行驶的轨迹，这个轨迹会交给控制模块，控制模块通过油门，刹车和方向盘使得车辆按照规划的轨迹运行。
本文由社区荣誉布道师——贺志国撰写，对Planning模块源代码进行了详细讲解，希望这篇文给感兴趣的同学带来更多帮助。
以下，ENJOY
规划模块的轨迹是短期轨迹，即车辆短期内行驶的轨迹，长期轨迹是Routing模块规划出的导航轨迹，即起点到目的地的轨迹，规划模块会先生成导航轨迹，然后根据导航轨迹和路况的情况，沿着短期轨迹行驶，直到目的地。
规划模块内部结构及其与其他模块的交互示意如下图所示。
根据各功能模块的启动过程的分析，Planning模块的主入口为：
1int main(int argc, char** argv) {
2 google::SetUsageMessage("we use this program to load dag and run user apps.");
3
4 // parse the argument
5 ModuleArgument module_args;
6 module_args.ParseArgument(argc, argv);
7
8 // initialize cyber
9 apollo::cyber::Init(argv[0]);
10
11 // start module
12 ModuleController controller(module_args);
13 if (!controller.Init()) {
14 controller.Clear();
15 AERROR << "module start error.";
16 return -1;
17 }
18
19 apollo::cyber::WaitForShutdown();
20 controller.Clear();
21 AINFO << "exit mainboard.";
22
23 return 0;
24}
Main函数十分简单，首先是解析参数，初始化Cyber环境，接下来创建一个ModuleController类对象controller，之后调用controller.Init()启动相关功能模块。进入Cyber RT的消息循环，等待cyber::WaitForShutdown()返回，清理资源并退出Main函数。ModuleController::Init()函数内部调用了ModuleController::LoadAll()函数：
1bool ModuleController::LoadAll() {
2 const std::string work_root = common::WorkRoot();
3 const std::string current_path = common::GetCurrentPath();
4 const std::string dag_root_path = common::GetAbsolutePath(work_root, "dag");
5
6 for (auto& dag_conf : args_.GetDAGConfList()) {
7 std::string module_path = "";
8 if (dag_conf == common::GetFileName(dag_conf)) {
9 // case dag conf argument var is a filename
10 module_path = common::GetAbsolutePath(dag_root_path, dag_conf);
11 } else if (dag_conf[0] == '/') {
12 // case dag conf argument var is an absolute path
13 module_path = dag_conf;
14 } else {
15 // case dag conf argument var is a relative path
16 module_path = common::GetAbsolutePath(current_path, dag_conf);
17 if (!common::PathExists(module_path)) {
18 module_path = common::GetAbsolutePath(work_root, dag_conf);
19 }
20 }
21 AINFO << "Start initialize dag: " << module_path;
22 if (!LoadModule(module_path)) {
23 AERROR << "Failed to load module: " << module_path;
24 return false;
25 }
26 }
27 return true;
28}
上述函数处理一个dag_conf配置文件循环，读取配置文件中的所有dag_conf，并逐一调用bool ModuleController::LoadModule(const std::string& path)函数加载功能模块。
使用工厂模式动态创建apollo::planning::PlanningComponent类对象，应先生成一个与之对应的工厂类，并将其加入到工厂集合类std::map<:string>,>中。
进一步展开：
1#define CLASS_LOADER_REGISTER_CLASS_INTERNAL(Derived, Base, UniqueID) \
2 namespace { \
3 struct ProxyType##UniqueID { \
4 ProxyType##UniqueID() { \
5 apollo::cyber::class_loader::utility::RegisterClass( \
6 #Derived, #Base); \
7 } \
8 }; \
9 static ProxyType##UniqueID g_register_class_##UniqueID; \
10 }
,>将PlanningComponent代入，最终得到：
1 namespace {
2 struct ProxyType__COUNTER__ {
3 ProxyType__COUNTER__() {
4 apollo::cyber::class_loader::utility::RegisterClass<planningcomponent, apollo::cyber::componentbase>(
5 "PlanningComponent", "apollo::cyber::ComponentBase");
6 }
7 };
8 static ProxyType__COUNTER__ g_register_class___COUNTER__;
9 }
</planningcomponent, apollo::cyber::componentbase>创建一个模板类utility::ClassFactory
第一部分介绍模块主入口时，提及bool ModuleController::LoadModule(const std::string& path)函数，正是该函数动态创建出了apollo::planning::PlanningComponent类对象。
函数内部调用分析如下：
1bool ModuleController::LoadModule(const std::string& path) {
2 DagConfig dag_config;
3 if (!common::GetProtoFromFile(path, &dag_config)) {
4 AERROR << "Get proto failed, file: " << path;
5 return false;
6 }
7 return LoadModule(dag_config);
8}
上述函数从磁盘配置文件读取配置信息，并调用bool ModuleController::LoadModule(const DagConfig& dag_config)函数加载功能模块：
1bool ModuleController::LoadModule(const DagConfig& dag_config) {
2 const std::string work_root = common::WorkRoot();
3
4 for (auto module_config : dag_config.module_config()) {
5 std::string load_path;
6 // ...
7 class_loader_manager_.LoadLibrary(load_path);
8 for (auto& component : module_config.components()) {
9 const std::string& class_name = component.class_name();
10 std::shared_ptr base =
11 class_loader_manager_.CreateClassObj(class_name);
12 if (base == nullptr) {
13 return false;
14 }
15
16 if (!base->Initialize(component.config())) {
17 return false;
18 }
19 component_list_.emplace_back(std::move(base));
20 }
21
22 // ...
23 }
24 return true;
25}
工厂类对象指针找到后，使用classobj = factory->CreateObj();就顺理成章地将PlanningComponent类对象创建出来了。
规划分为两种模式：车道规划和导航规划，包含四种具体规划算法：PublicRoadPlanner、LatticePlanner、NaviPlanner、RTKPlanner，具体类图如下所示：
PublicRoadPlanner算法从Routing模块输出的高精地图Lane序列获得全局导航路径。
基于场景、阶段和任务的理念进行规划，优点是能合理有效地应对每种场景，易于扩充，并且基于配置文件动态增减场景、阶段及使用的任务，灵活性强；缺点是可能会遗漏一些特殊场景，但可通过不断扩充新的场景加以解决。
该算法的主要执行流程如下：
可借助GDB调试命令对上述执行流程进行更为深入的理解，例如TrafficLightProtectedStageApproach阶段的PathLaneBorrowDecider任务的调用堆栈，从下往上看，对于任意一个任务的调用流程一目了然：
1#0 apollo::planning::PathLaneBorrowDecider::Process (this=0x7f8c28294460, frame=0x7f8c38029f70,
2 reference_line_info=0x7f8c3802b140) at modules/planning/tasks/deciders/path_lane_borrow_decider/path_lane_borrow_decider.cc:39
3#1 0x00007f8c0468b7c8 in apollo::planning::Decider::Execute (this=0x7f8c28294460, frame=0x7f8c38029f70,
4 reference_line_info=0x7f8c3802b140) at modules/planning/tasks/deciders/decider.cc:31
5#2 0x00007f8c065c4a01 in apollo::planning::scenario::Stage::ExecuteTaskOnReferenceLine (this=0x7f8c28293eb0,
6 planning_start_point=..., frame=0x7f8c38029f70) at modules/planning/scenarios/stage.cc:96
7#3 0x00007f8c06e721da in apollo::planning::scenario::traffic_light::TrafficLightProtectedStageApproach::Process (
8 this=0x7f8c28293eb0, planning_init_point=..., frame=0x7f8c38029f70) at
9 modules/planning/scenarios/traffic_light/protected/stage_approach.cc:48
10#4 0x00007f8c067f1732 in apollo::planning::scenario::Scenario::Process (
11 this=0x7f8c2801bf20, planning_init_point=..., frame=0x7f8c38029f70)
12 at modules/planning/scenarios/scenario.cc:76
13#5 0x00007f8c186e153a in apollo::planning::PublicRoadPlanner::Plan (
14 this=0x23093de0, planning_start_point=..., frame=0x7f8c38029f70,
15 ptr_computed_trajectory=0x7f8b9a5fbed0) at modules/planning/planner/public_road/public_road_planner.cc:51
16#6 0x00007f8c19ee5937 in apollo::planning::OnLanePlanning::Plan (
17 this=0x237f3b0, current_time_stamp=1557133995.3679764, stitching_trajectory=std::vector of length 1,
18 capacity 1 = {...}, ptr_trajectory_pb=0x7f8b9a5fbed0) at modules/planning/on_lane_planning.cc:436
19#7 0x00007f8c19ee40fa in apollo::planning::OnLanePlanning::RunOnce (
20 this=0x237f3b0, local_view=..., ptr_trajectory_pb=0x7f8b9a5fbed0) at modules/planning/on_lane_planning.cc:304
21#8 0x00007f8c1ab0d494 in apollo::planning::PlanningComponent::Proc (
22 this=0x1d0f310, prediction_obstacles=std::shared_ptr (count 4, weak 0) 0x7f8b840164f8,
23 chassis=std::shared_ptr (count 4, weak 0) 0x7f8b84018a08,
24 localization_estimate=std::shared_ptr (count 4, weak 0) 0x7f8b8400d3b8) at modules/planning/planning_component.cc:134
25#9 0x00007f8c1abb46c4 in apollo::cyber::Component<apollo::prediction::predictionobstacles, < span="">
26 apollo::canbus::Chassis, apollo::localization::LocalizationEstimate, apollo::cyber::NullType>::Process (this=0x1d0f310,
27 msg0=std::shared_ptr (count 4, weak 0) 0x7f8b840164f8, msg1=std::shared_ptr (count 4, weak 0) 0x7f8b84018a08,
28 msg2=std::shared_ptr (count 4, weak 0) 0x7f8b8400d3b8) at ./cyber/component/component.h:291
29#10 0x00007f8c1aba2698 in apollo::cyber::Component<apollo::prediction::predictionobstacles, < span="">
30 apollo::canbus::Chassis, apollo::localization::LocalizationEstimate, apollo::cyber::NullType>::Initialize(
31 apollo::cyber::proto::ComponentConfig const&)::{lambda(std::shared_ptr<apollo::prediction::predictionobstacles> const&,
32 std::shared_ptr<:canbus::chassis> const&, std::shared_ptr<apollo::localization::localizationestimate> const&)#2}::operator()
33 (std::shared_ptr<apollo::prediction::predictionobstacles> const&, std::shared_ptr<:canbus::chassis> const&,
34 std::shared_ptr<apollo::localization::localizationestimate> const&) const (__closure=0x2059a430,
35 msg0=std::shared_ptr (count 4, weak 0) 0x7f8b840164f8, msg1=std::shared_ptr (count 4, weak 0) 0x7f8b84018a08,
36 msg2=std::shared_ptr (count 4, weak 0) 0x7f8b8400d3b8) at ./cyber/component/component.h:378
37#11 0x00007f8c1abb4ad2 in apollo::cyber::croutine::RoutineFactory apollo::cyber::croutine::CreateRoutineFactory
38 <apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
39 apollo::cyber::Component<apollo::prediction::predictionobstacles, apollo::canbus::chassis,
40 apollo::localization::LocalizationEstimate, apollo::cyber::NullType>::Initialize(
41 apollo::cyber::proto::ComponentConfig const&)::{lambda(std::shared_ptr<apollo::prediction::predictionobstacles> const&,
42 std::shared_ptr<:canbus::chassis> const&, std::shared_ptr<apollo::localization::localizationestimate> const&)#2}&>
43 (apollo::cyber::Component<apollo::prediction::predictionobstacles, apollo::canbus::chassis,
44 apollo::localization::LocalizationEstimate, apollo::cyber::NullType>::Initialize(apollo::cyber::proto::ComponentConfig const&)::
45 {lambda(std::shared_ptr<apollo::prediction::predictionobstacles> const&, std::shared_ptr<:canbus::chassis> const&,
46 std::shared_ptr<apollo::localization::localizationestimate> const&)#2}&,
47 std::shared_ptr<apollo::cyber::data::datavisitor<apollo::prediction::predictionobstacles,
48 apollo::canbus::Chassis, apollo::localization::LocalizationEstimate, apollo::cyber::NullType> > const&)::
49 {lambda()#1}::operator()() const::{lambda()#1}::operator()() const (__closure=0x2059a420) at ./cyber/croutine/routine_factory.h:108
50#12 0x00007f8c1ac0466a in std::_Function_handler<void (), apollo::cyber::croutine::routinefactory < span="">
51apollo::cyber::croutine::CreateRoutineFactory<apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
52apollo::cyber::Component<apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
53apollo::cyber::NullType>::Initialize(apollo::cyber::proto::ComponentConfig const&)::{lambda(std::shared_ptr<apollo::prediction::predictionobstacles> const&,
54std::shared_ptr<:canbus::chassis> const&, std::shared_ptr<apollo::localization::localizationestimate> const&)#2}&>
55(apollo::cyber::Component<apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
56apollo::cyber::NullType>::Initialize(apollo::cyber::proto::ComponentConfig const&)::{lambda(std::shared_ptr<apollo::prediction::predictionobstacles> const&,
57std::shared_ptr<:canbus::chassis> const&, std::shared_ptr<apollo::localization::localizationestimate> const&)#2}&,
58std::shared_ptr<apollo::cyber::data::datavisitor<apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
59apollo::cyber::NullType> > const&)::{lambda()#1}::operator()() const::{lambda()#1}>::_M_invoke(std::_Any_data const&) (__functor=...) at
60/usr/include/c++/4.8/functional:2071
61#13 0x00007f8c5f5b86e8 in std::function::operator()() const (this=0x205f1160) at /usr/include/c++/4.8/functional:2471
62#14 0x00007f8c57560cbc in apollo::cyber::croutine::CRoutine::Run (this=0x205f1148) at ./cyber/croutine/croutine.h:143
63#15 0x00007f8c5755ff55 in apollo::cyber::croutine::(anonymous namespace)::CRoutineEntry (arg=0x205f1148) at cyber/croutine/croutine.cc:43
</apollo::cyber::data::datavisitor<apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
</apollo::localization::localizationestimate></apollo::prediction::predictionobstacles></apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
</apollo::localization::localizationestimate></apollo::prediction::predictionobstacles></apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
</apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
</void (), apollo::cyber::croutine::routinefactory <></apollo::cyber::data::datavisitor<apollo::prediction::predictionobstacles,
</apollo::localization::localizationestimate></apollo::prediction::predictionobstacles></apollo::prediction::predictionobstacles, apollo::canbus::chassis,
</apollo::localization::localizationestimate></apollo::prediction::predictionobstacles></apollo::prediction::predictionobstacles, apollo::canbus::chassis,
</apollo::prediction::predictionobstacles, apollo::canbus::chassis, apollo::localization::localizationestimate,
</apollo::localization::localizationestimate></apollo::prediction::predictionobstacles></apollo::localization::localizationestimate></apollo::prediction::predictionobstacles></apollo::prediction::predictionobstacles, <></apollo::prediction::predictionobstacles, <>所有规划算法共用的流程略去不表，与PublicRoadPlanner规划算法相关的有两处，一处是PublicRoadPlanner::Init，另一处是PublicRoadPlanner::Plan。
下面来看场景更新函数ScenarioManager::Update的代码：
1void ScenarioManager::Update(const common::TrajectoryPoint& ego_point,
2 const Frame& frame) {
3 CHECK(!frame.reference_line_info().empty());
4 Observe(frame);
5 ScenarioDispatch(ego_point, frame);
6}
该函数包含两个子函数：ScenarioManager::Observe和ScenarioManager::ScenarioDispatch，其中前者用于更新first_encountered_overlap_map_，代码如下所示：
1void ScenarioManager::Observe(const Frame& frame) {
2 // init first_encountered_overlap_map_
3 first_encountered_overlap_map_.clear();
4 const auto& reference_line_info = frame.reference_line_info().front();
5 const auto& first_encountered_overlaps =
6 reference_line_info.FirstEncounteredOverlaps();
7 for (const auto& overlap : first_encountered_overlaps) {
8 if (overlap.first == ReferenceLineInfo::PNC_JUNCTION ||
9 overlap.first == ReferenceLineInfo::SIGNAL ||
10 overlap.first == ReferenceLineInfo::STOP_SIGN ||
11 overlap.first == ReferenceLineInfo::YIELD_SIGN) {
12 first_encountered_overlap_map_[overlap.first] = overlap.second;
13 }
14 }
15}
* 以上内容为开发者原创，不代表百度官方言论。
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