Body: <p>I am required to obtain data through a sensor located on the vehicle reading speed, vibration, roll and tilt, within a sample time, to classify the current road condition using machine learning for a high school project. </p>
  
  <p>Which algorithm/approach may be most suitable for this task? Suggestions to sources for learning (books, tutorials) would be also appreciated, as I am new to AI and ML.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-05T01:05:06.777Z
Comments.Text: Hi and welcome to AI SE! You could start with a feed-forward neural network (FFNN), where your features (input variables) are the speed, vibration, roll, etc., and your output (dependent variable) is the class (i.e. the condition of the road). Of course, you will need a labelled dataset. Have a look at any tutorial that shows the usage of a FFNN (e.g. with Keras).
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-05T18:23:23.753Z
Comments.Text: A resource for learning might be the Stanley paper http://isl.ecst.csuchico.edu/DOCS/darpa2005/DARPA%202005%20Stanley.pdf . Terrain analysis is studied from section 5 onward.
Comments.UserDisplayName: fabian
Tags: <neural-networks><machine-learning><computer-vision><pattern-recognition>
Title: Suitable algorithms for classifying terrain condition (asphalt, dirt etc) for motor vehicles
UserDisplayName: Navoda Bandara
ThreadText: # Suitable algorithms for classifying terrain condition (asphalt, dirt etc) for motor vehicles
  
  (Asked by: username_0 on 2020-03-03)
  
  I am required to obtain data through a sensor located on the vehicle reading speed, vibration, roll and tilt, within a sample time, to classify the current road condition using machine learning for a high school project.
  
  Which algorithm/approach may be most suitable for this task? Suggestions to sources for learning (books, tutorials) would be also appreciated, as I am new to AI and ML.
  
  **Tags:** neural-networks, machine-learning, computer-vision, pattern-recognition
  
  > **Comments:**
  > **username_1**: Hi and welcome to AI SE! You could start with a feed-forward neural network (FFNN), where your features (input variables) are the speed, vibration, roll, etc., and your output (dependent variable) is the class (i.e. the condition of the road). Of course, you will need a labelled dataset. Have a look at any tutorial that shows the usage of a FFNN (e.g. with Keras).  
  > **username_2**: A resource for learning might be the Stanley paper http://isl.ecst.csuchico.edu/DOCS/darpa2005/DARPA%202005%20Stanley.pdf . Terrain analysis is studied from section 5 onward.  
  
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  ## 0 answers
  
  