The prediction module should also be able to learn new behaviors. When there is a lot of traffic on the road, the road conditions become very complex and trying to develop a static model for each scenario at this point is a nearly impossible task. This is why when faced with a problem like this, we need to The prediction module is capable of learning new behaviors. With this approach, we can train using multiple sources of data, which allows the algorithm to improve its predictive power over time.
Common prediction methods
Now we are going to learn the architecture of Apollo platform for prediction. Model-based forecasting together with Data-driven forecasting are the two basic types of predictions.
To make predictions more easily in specific environments (e.g., highways, etc.), we can use this almost infinite number of trajectories to limit the prediction problem. This will make it easier for the car to store the discontinuous lane information it retains at any point in time, so we will simplify the prediction and make it easy to control. moreover Forecasting will also be at the heart of decision making that Because you'll make better decisions if you know what to expect.
Suppose the unmanned car comes to a T-intersection and sees a car coming from the left, at which point it is not clear whether the car is going to turn right or go straight.