The Prediction module studies and predicts the behavior of all the obstacles detected by the perception module. Prediction receives obstacle data along with basic perception information including positions, headings, velocities, accelerations, and then generates predicted trajectories with probabilities for those obstacles.
Note:
The Prediction module only predicts the behavior of obstacles and not the EGO car. The Planning module plans the trajectory of the EGO car.
- Obstacles information from the perception module
- Localization information from the localization module
- Planning trajectory of the previous computing cycle from the planning module
- Obstacles annotated with predicted trajectories and their priorities. Obstacle priority is now calculated as individual scenarios are prioritized differently. The priorities include: ignore, caution and normal (default)
Based on the figure below, the prediction module comprises of 4 main functionalities: Container, Scenario, Evaluator and Predictor. Container, Evaluator and Predictor existed in Apollo 3.0. In Apollo 3.5, we introduced the Scenario functionality as we have moved towards a more scenario-based approach for Apollo's autonomous driving capabilities.
Container stores input data from subscribed channels. Current supported inputs are perception obstacles, vehicle localization and vehicle planning.
The Scenario sub-module analyzes scenarios that includes the ego vehicle. Currently, we have two defined scenarios:
- Cruise : this scenario includes Lane keeping and following.
- Junction : this scenario involves junctions. Junctions can either have traffic lights and/or STOP signs.
The Evaluator predicts path and speed separately for any given obstacle. An evaluator evaluates a path by outputting a probability for it (lane sequence) using the given model stored in prediction/data/.
There exists 5 types of evaluators, two of which were added in Apollo 3.5. As Cruise and Junction scenarios have been included, their corresponding evaluators (Cruise MLP and Junction MLP) were added as well. The list of available evaluators include:
-
Cost evaluator: probability is calculated by a set of cost functions
-
MLP evaluator: probability is calculated using an MLP model
-
RNN evaluator: probability is calculated using an RNN model
-
Cruise MLP + CNN-1d evaluator: probability is calculated using a mix of MLP and CNN-1d models for the cruise scenario
-
Junction MLP evaluator: probability is calculated using an MLP model for junction scenario
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
The prediction module estimates the future motion trajectories for all perceived obstacles. The output prediction message wraps the perception information. Prediction both subscribes to and is triggered by perception obstacle messages, as shown below:
The prediction module also takes messages from both localization and planning as input. The structure is shown below:
