Answers.Body: <p>What you’re looking for is outlier detection.  Telling users apart may not be super easy in this scenario.  The team could proceed from the assumption the users can be identified otherwise (what Bluetooth is connected or a user profile logged into the head unit).  This is a reasonable assumption in modern luxury cars (where new tech gets tested).  Then perform outlier detection on a per user basis. This will make the algorithm slightly more likely to succeed.</p>
  <p>For extra realism be careful of large neural networks.  The compute doesn’t really exist currently in vehicle.</p>
  
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Answers.CreationDate: 2024-08-25T20:53:41.240Z
Answers.Text: Excuse my lack of knowledge, I am learning about the field. I had a sense that it would be tough but I did not know to what extent the difficulty was, what makes this a difficult project? I thought if each member of the team drove on a gaming steering wheel on a simulator for a substantial amount of time we would have the required dataset, I thought of a neural network with a SoftMax layer that classifies so I thought it is doable. I think there is the Ocslab driving dataset as well. Or potentially fine-tuning an existing model to our training set but I have not researched enough about it.
Answers.UserDisplayName: Omar Rida
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Answers.CreationDate: 2024-08-26T12:14:58.123Z
Answers.Text: You have the right approach.  Driving patterns are in response to the world.  Unless two divers are fingerprinted on a closed course I would be surprised if this system could differentiate two similar drivers.  But ultimately it’s a school project, have fun and learn something new.
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Answers.UserDisplayName: foreverska
Body: <p>I am an electronics and communications engineer, in my graduation project we opted (a team of 6) to identify if the car's user is the one driving or is it possibly a thief driving judging from the driving/behavior analysis and pattern, using some features that are obtained from the car (i.e. steering wheel angle, avg speed of the car, how frequent he/she changes lanes etc.) and judging by those features the model should give a reasonable estimate on who is driving, the problem is, we don't know where to start that much, how do we approach such a problem, what networks/models are suitable or topics/things do we need to learn? it is a classification problem so I initially thought of a softmax (for multiple users of the car) or a sigmoid output layer in a neural network. We are still a bit new to the field, and some of us want to specialize more in the field (I do) and have so far finished Andrew Ng's Machine Learning specialization, but we are very eager to learn more.</p>
  <p>Thanks in advance.</p>
  
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Comments.CreationDate: 2024-09-02T08:49:03.207Z
Comments.Text: Can you please put your **specific question** in the title and try to narrow it as much as possible? Asking things like "How should we do this project?" are a bit too broad. I'd recommend you ask specific questions, each in their own posts, about the specific doubts that you have.
Comments.UserDisplayName: nbro
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Comments.CreationDate: 2024-09-02T08:49:51.873Z
Comments.Text: Having said that, it looks like you want to classify the driver into 2 classes, owner and thief, so this would be a classification problem, and you would use different features. In principle, you can use different classification models (neural networks are an option). You need to understand 1. how much data you have or you can collect, 2. is the data laballed? if not, are you able to label it? You can also use unsupervised learning here. Maybe this answers your actual specific question here. In that case, let me know and I will write this as a formal anwer below.
Comments.UserDisplayName: nbro
Tags: <neural-networks><machine-learning><autonomous-vehicles>
Title: An anti-theft project that should identify user's driving pattern
UserDisplayName: Omar Rida
ThreadText: # An anti-theft project that should identify user's driving pattern
  
  (Asked by: username_0 on 2024-08-25)
  
  I am an electronics and communications engineer, in my graduation project we opted (a team of 6) to identify if the car's user is the one driving or is it possibly a thief driving judging from the driving/behavior analysis and pattern, using some features that are obtained from the car (i.e. steering wheel angle, avg speed of the car, how frequent he/she changes lanes etc.) and judging by those features the model should give a reasonable estimate on who is driving, the problem is, we don't know where to start that much, how do we approach such a problem, what networks/models are suitable or topics/things do we need to learn? it is a classification problem so I initially thought of a softmax (for multiple users of the car) or a sigmoid output layer in a neural network. We are still a bit new to the field, and some of us want to specialize more in the field (I do) and have so far finished Andrew Ng's Machine Learning specialization, but we are very eager to learn more.
  
  Thanks in advance.
  
  **Tags:** neural-networks, machine-learning, autonomous-vehicles
  > **Comments:**
  > **username_1**: Can you please put your **specific question** in the title and try to narrow it as much as possible? Asking things like "How should we do this project?" are a bit too broad. I'd recommend you ask specific questions, each in their own posts, about the specific doubts that you have.  
  > **username_1**: Having said that, it looks like you want to classify the driver into 2 classes, owner and thief, so this would be a classification problem, and you would use different features. In principle, you can use different classification models (neural networks are an option). You need to understand 1. how much data you have or you can collect, 2. is the data laballed? if not, are you able to label it? You can also use unsupervised learning here. Maybe this answers your actual specific question here. In that case, let me know and I will write this as a formal anwer below.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-08-25. Score: 1
  
  What you’re looking for is outlier detection. Telling users apart may not be super easy in this scenario. The team could proceed from the assumption the users can be identified otherwise (what Bluetooth is connected or a user profile logged into the head unit). This is a reasonable assumption in modern luxury cars (where new tech gets tested). Then perform outlier detection on a per user basis. This will make the algorithm slightly more likely to succeed.
  
  For extra realism be careful of large neural networks. The compute doesn’t really exist currently in vehicle.
  
  > **Comments:**
  > **username_0**: Excuse my lack of knowledge, I am learning about the field. I had a sense that it would be tough but I did not know to what extent the difficulty was, what makes this a difficult project? I thought if each member of the team drove on a gaming steering wheel on a simulator for a substantial amount of time we would have the required dataset, I thought of a neural network with a SoftMax layer that classifies so I thought it is doable. I think there is the Ocslab driving dataset as well. Or potentially fine-tuning an existing model to our training set but I have not researched enough about it.  
  > **username_2**: You have the right approach.  Driving patterns are in response to the world.  Unless two divers are fingerprinted on a closed course I would be surprised if this system could differentiate two similar drivers.  But ultimately it’s a school project, have fun and learn something new.  
  
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