PREDICTION PREDICTOR

Introduction

The prediction module comprises 4 main functionalities: Container, Scenario, Evaluator and Predictor.

Predictor generates predicted trajectories for obstacles. Currently, the supported predictors include:

Empty: obstacles have no predicted trajectories
Single lane: Obstacles move along a single lane in highway navigation mode. Obstacles not on lane will be ignored.
Lane sequence: obstacle moves along the lanes
Move sequence: obstacle moves along the lanes by following its kinetic pattern
Free movement: obstacle moves freely
Regional movement: obstacle moves in a possible region
Junction: Obstacles move toward junction exits with high probabilities
Interaction predictor: compute the likelihood to create posterior prediction results after all evaluators have run. This predictor was created for caution level obstacles
Extrapolation predictor: extends the Semantic LSTM evaluator's results to create an 8 sec trajectory.

Here, we mainly introduce three typical predictors，extrapolation predictor, move sequence predictor and interaction predictor，other predictors are similar to them.

Where is the code

Please refer prediction predictor.

Code Reading

Extrapolation predictor

This predictor is used to extend the Semantic LSTM evaluator's results to creat a long-term trajectroy(which is 8 sec).

There are two main kinds of extrapolation, extrapolate by lane and extrapolate by free move.

Base on a search radium and an angle threshold, which can be changed in perdiction config, we get most likely lane that best matches the short-term predicted trajectory obtained from Semantic LSTM evaluator.

cpp

       LaneSearchResult SearchExtrapolationLane(const Trajectory& trajectory,
                                                const int num_tail_point);

If the matching lane is found, we extend short-term predicted trajectory by lane.

Firstly, we remove points those are not in the matching lane.
Then, we project the modified short-term predicted trajectory onto the matching lane to get SL info.

cpp

       static bool GetProjection(
       const Eigen::Vector2d& position,
       const std::shared_ptr<const hdmap::LaneInfo> lane_info, double* s,double* l);

According to prediction horizon, we extend the modified short-term predicted trajectory along the mathcing lane with a constant-velocity module and get smooth points from lane.

cpp

       static bool SmoothPointFromLane(const std::string& id, const double s,
                                       const double l, Eigen::Vector2d* point,
                                       double* heading);

Note that the extraplation speed, which is used in constant-velocity module, is calculated by calling ComputeExtraplationSpeed.

cpp

       void ExtrapolateByLane(const LaneSearchResult& lane_search_result,
                              const double extrapolation_speed,
                              Trajectory* trajectory_ptr,
                              ObstacleClusters* clusters_ptr);

cpp

       double ComputeExtraplationSpeed(const int num_tail_point,
                                       const Trajectory& trajectory);

Otherwise we use free move module to extend.

cpp

  void ExtrapolateByFreeMove(const int num_tail_point,
                             const double extrapolation_speed,
                             Trajectory* trajectory_ptr);

Move sequence predictor

Obstacle moves along the lanes by its kinetic pattern.
Ingore those lane sequences with lower probability.

cpp

   void FilterLaneSequences(
       const Feature& feature, const std::string& lane_id,
       const Obstacle* ego_vehicle_ptr,
       const ADCTrajectoryContainer* adc_trajectory_container,
       std::vector<bool>* enable_lane_sequence);

If there is a stop sign on the lane, we check whether ADC supposed to stop.

cpp

bool SupposedToStop(const Feature& feature, const double stop_distance,
                   double* acceleration);

If ADC is about to stop, we produce trajectroy with constant-acceleration module.

cpp

  void DrawConstantAccelerationTrajectory(
      const Obstacle& obstacle, const LaneSequence& lane_sequence,
      const double total_time, const double period, const double acceleration,
      std::vector<apollo::common::TrajectoryPoint>* points);

Otherwise we produce trajectory by obstacle's kinetic pattern.

cpp

  bool DrawMoveSequenceTrajectoryPoints(
      const Obstacle& obstacle, const LaneSequence& lane_sequence,
      const double total_time, const double period,
      std::vector<apollo::common::TrajectoryPoint>* points);

Interaction predictor

Compute the likelihood to create posterier prediction results after all evaluators have run. This predictor was created for caution level obstacles.
Sampling ADC trajectory at a fixed interval(which can be changed in prediction gflag file).

cpp

   void BuildADCTrajectory(
     const ADCTrajectoryContainer* adc_trajectory_container,
     const double time_resolution);

Compute trajectory cost for each short-term predicted trajectory. The trajectory cost is weighted cost from different trajectory evluation metrics, such as acceleration, centripetal acceleration and collsion cost, which can be written in the following form:

   total_cost = w_acc * cost_acc + w_centri * cost_centri + w_collision * cost_collision

Note that, the collsion cost is calucalated by the distance between ADC and obstacles.

cpp

   double ComputeTrajectoryCost(
       const Obstacle& obstacle, const LaneSequence& lane_sequence,
       const double acceleration,
       const ADCTrajectoryContainer* adc_trajectory_container);

We use the following equration to compute the likelihood for each short-term predicted trajectory.

   likelihood = exp (-alpha * total_cost), the alpha can be changed in prediction gflag file.

cpp

   double ComputeLikelihood(const double cost);

Base on the likelihood, we get the posterier prediction results.

cpp

   double ComputePosterior(const double prior, const double likelihood);