This is an area where ANN can help you greatly. My interpretations of your goals is to make them operate on the basis that a human does. And for this ANN are just so perfect. I can understand how you are wary of them since you haven't researched them and from a few google searches they may seem too abstract and not very practical, but bare with me here and so you can see the same light I do.
Your method is a cure of the symptoms rather than a cure for the cause. You can go down this route but it will never be as genuine or appropriate as ANN. For instance, reaction times have a lot less to do with racing than you might think. Jeremy Clarkson I believe did a documentary on it and found that F1 drivers actually have the same reaction times as the rest of us. Where they differ is in their experience, just like a professional football (soccer) goalkeeper reacts to the smallest of cues in order to predict where the ball will go. This is working on experience to judge BEFORE the event happens the best course of action rather than working at the limit with superhuman reaction or slightly before as is your technique to try and 'cover up' the superhuman effect. As ANN work on experience there is no need to cover up their ability because they are genuinely functioning as a human would. So the problem is eliminated. If you see what I mean.
Let me just run you through the basics of it. You have 3 layers, input nodes, hidden layer nodes and output nodes. They are self explanatory. The input nodes correspond to the inputs a human would have, the hidden layer is the 'brain' which encodes experience into a purely parallel processing unit which uses the output nodes (hands, feet etc) to produce the action or in this case control the car.
Here is what immediately strikes me as necessary;
Input;
Vision
Feeling - Steering Wheel, G-forces, Pedals, Seat,
Sound - Engine, Tyres [FL, FR, RL, RR],
Output;
Steering Movement - Steering Angle
Pedal Movement - Accelerator Pressure, Brake Pressure
Head Movement - Left/Right
Gearbox - Shift Up, Shift Down
I have not included the hidden layer because in practice it is not an exact science, just like engine manufacturers find ignition timing maps by trial and error so do ANN find the best configuration for the hidden layer. Don't get disheartened there are only a few variables to control namely, the number of nodes and the learning rate (how big you want the changes to be within trial and error). You start off with the minimum number (i.e. 1) and then slowly increase until the desired behaviour is acceptable.
The concept is very easy to understand, just like the gates on a CPU work extremely simply so do ANN, but what is astonishing is when you put them in action you can see how easily the human psyche can be modelled. In fact it gave me a lot of food for thought about my own existence, and whether or not a good ANN would indeed be concious.
This area is highly worth pursing for you if you can spend a few hours researching it.
Your method is a cure of the symptoms rather than a cure for the cause. You can go down this route but it will never be as genuine or appropriate as ANN. For instance, reaction times have a lot less to do with racing than you might think. Jeremy Clarkson I believe did a documentary on it and found that F1 drivers actually have the same reaction times as the rest of us. Where they differ is in their experience, just like a professional football (soccer) goalkeeper reacts to the smallest of cues in order to predict where the ball will go. This is working on experience to judge BEFORE the event happens the best course of action rather than working at the limit with superhuman reaction or slightly before as is your technique to try and 'cover up' the superhuman effect. As ANN work on experience there is no need to cover up their ability because they are genuinely functioning as a human would. So the problem is eliminated. If you see what I mean.
Let me just run you through the basics of it. You have 3 layers, input nodes, hidden layer nodes and output nodes. They are self explanatory. The input nodes correspond to the inputs a human would have, the hidden layer is the 'brain' which encodes experience into a purely parallel processing unit which uses the output nodes (hands, feet etc) to produce the action or in this case control the car.
Here is what immediately strikes me as necessary;
Input;
Vision
Feeling - Steering Wheel, G-forces, Pedals, Seat,
Sound - Engine, Tyres [FL, FR, RL, RR],
Output;
Steering Movement - Steering Angle
Pedal Movement - Accelerator Pressure, Brake Pressure
Head Movement - Left/Right
Gearbox - Shift Up, Shift Down
I have not included the hidden layer because in practice it is not an exact science, just like engine manufacturers find ignition timing maps by trial and error so do ANN find the best configuration for the hidden layer. Don't get disheartened there are only a few variables to control namely, the number of nodes and the learning rate (how big you want the changes to be within trial and error). You start off with the minimum number (i.e. 1) and then slowly increase until the desired behaviour is acceptable.
The concept is very easy to understand, just like the gates on a CPU work extremely simply so do ANN, but what is astonishing is when you put them in action you can see how easily the human psyche can be modelled. In fact it gave me a lot of food for thought about my own existence, and whether or not a good ANN would indeed be concious.
This area is highly worth pursing for you if you can spend a few hours researching it.
), pointing "forward," away from yourself who's sitting in front of the monitor. Just sayin'