README.md

Prediction

Introduction

The prediction module receives the obstacles from the perception module with their basic perception information including positions, headings, velocities, accelerations, and generates the predicted trajectories with probabilities for the obstacles.

Input

• Obstacles from perception module
• Localizaton from localization module

Output

• Obstacles additionally with predicted trajectories

Functionalities

• Container

  Container stores input data from subscribed channels. Current supported inputs are perception obstacles, vehicle localization and vehicle planning.

• Evaluator

  Evaluator predicts path and speed separately for any given obstacles. An evaluator evaluates a path by outputing a probability for it (lane sequence) using the given model stored in prediction/data/.

  Three types of evaluators will be provided including:

  • Cost evaluator: probability is calculated by a set of cost functions

  • MLP evaluator: probability is calculated with an MLP model

  • RNN evaluator: probability is calculated with an RNN model

• Predictor

  Predictor generates predicted trajectories for obstacles. Currently supported predictor includes:

  • 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