motion planning for self-driving using deep learning,
I want to research a paper with deep learning (algorithm ) and the results of training and testing. full python project. using any python library that uses deep learning for motion planning for self-driving.
the topics recommended:
1. Sampling-Based Motion Planning with Learning in Autonomous Driving using deep learning .
recorces : https://dl.acm.org/doi/fullHtml/10.1145/3469086
2. motion prediction on self-driving cars using deep learning.
3. motion detection on self-driving cars using deep learning (It is an easy and very consuming topic that you have to add new things. )
no plagiarism.
no use the removing plagiarism tools.
New and unused research.
So for the deep learning model, I need :
0. Problem Definition (Technically)
1. Collecting Data
Make sure you use data from a reliable source, as it will directly affect the outcome of the model. Good data is relevant, contains very few missing and repeated values, and has a good representation of the various subcategories/classes present.
2. Preparing the Data.
After you have the data, you have to prepare it. You have done this by
Putting together all the data you have and randomizing it. This helps make sure that data is evenly distributed, and that the ordering does not affect the learning process.
Cleaning the data to remove unwanted data, missing values, rows, columns, duplicate values, data type conversion, etc. You might even have to restructure the dataset and change the rows and columns or index of rows and columns.
Visualize the data to understand how it is structured and understand the relationship between various variables and classes present.
Splitting the cleaned data into two sets a training set and a testing set. The training set is the set the model learns from. A testing set is used to check the accuracy of the model after training
3. Choosing a Model (only In deep learning -CNN).
you have to see if your model is suited for numerical or categorical data and choose accordingly first.
4. Training the Model.
you pass the prepared data to a deep learning model that you make to find patterns and make predictions. It results in the model learning from the data so that it can accomplish the task set.
5. Evaluating the Model:
check to see how its performing. This is done by testing the performance of the model on previously unseen data. The unseen data used is the testing set that you split our data into earlier. If testing was done on the same data which is used for training, you will not get an accurate measure, as the model is already used to the data, and finds the same patterns in it, as it previously did. This will give you disproportionately high accuracy.
6. Parameter Tuning:
Once you have created and evaluated the model, see if its accuracy can be improved in any way. This is done by tuning the parameters present in the model that you use it. Parameters are the variables in the model that you generally decide. At a particular value of your parameter, the accuracy will be the maximum. Parameter tuning refers to finding these values.
7. Making Predictions, In the end
you can use the model on unseen data to make predictions accurately.