Prediction Flashcards
How do we think about handling multi-modal uncertainty?
Maintaining some beliefs about how probable each potential mode is.
How are these multi-modal predictions represented?
Represented by a set of possible trajectories such as dotted lines and an associated probability for each trajectory.
Name 2 type of Prediction Technologies
- Model Based
2. Data Driven
Explain the Model Based Prediction Technique
Use Mathematical Models of Motion to predict trajectories .
Explain the Data Driven Based Prediction Technique
Rely on machine learning and examples to learn from.
Is Trajectory Clustering a Model Based or Data-Driven Prediction Technique?
Data Driven Approach
Are Process Models a Model Based or Data-Driven Prediction Technique?
Model Based Approach
Explain what a process model is?
This is a Model Based Approach.
A process model is a mathematical description of object motion for behavior.
Are Multi-Modal Estimators a Model Based or Data-Driven Prediction Technique?
Both.
What are Multi-Modal Estimators?
An effective technique for handling the uncertainty associated with prediction, namely, the uncertainty about which maneuver an object will do in a particular situation.
Explain the Hybrid Approaches.
Use data and process models to predict motion through a cycle of intent classification where we try to figure out what a driver wants to do. Trajectory Generation tries to figure out how they are likely to do it.
A prediction module uses what to generate predictions for what all other dynamic objects in view are likely to do?
A map and data from sensor fusion.
Which model is best for determining maximum safe turning speed on a wet road.
In this situation we could use a model based approach to incorporate our knowledge of physics (friction, forces, etc…) to figure out exactly (or almost exactly) when a vehicle would begin to skid on a wet road.
Which model is better at predicting the behavior of an unidentified object sitting on the road.
Data Driven. Even with data driven approaches this would still be a very hard problem but since we don’t even know what this object is, a model based approach to prediction would be nearly impossible.
Which model is better at predicting the behavior of a vehicle on a two lane highway in light traffic.
Hybrid Approach
Trajectory Clustering has two phases.
Offline and Online
Define the offline phase for trajectory clustering.
This is where the algorithm learns a model from data.
Define the online phase for trajectory clustering.
This is where it uses that model to generate predictions.
Name 5 Steps for Target Clustering Offline.
- Get a lot of trajectories.
- Clean the data.
- Define some mathematical measurement of similarity.
- Perform Unsupervised Clustering.
- Define prototype trajectories for each cluster
Name 3 Steps for Target Clustering Online.
For every update cycle:
- Observe vehicle’s partial trajectory.
- Compare to prototype trajectories for each cluster.
- Predict a trajectory.
1 & 2 comparison is done using the same similarity measurement used for offline clustering.
What are Frenet Coordinates?
It is a way representing position on a road in a more intuitive way than the traditional x, y Cartesian Coordinates.
Two main variables: s & d
Explain s in Frenet Coordinates.
s is the distance ALONG the road. Also known as the longitudinal displacement.
Explain d in Frenet Coordinates.
d represents side to side position on the road. Also known as the lateral displacement.
Data-Driven Approaches solve the prediction problem in 2 phases.
- Offline Training
2. Online Prediction
