Are self-driving cars using single frame or multiple frame to make decision? This might be a trivial question but I couldn't find any reliable answers on the internet.

Almost all the neural network architectures for self-driving cars that I have seen on the internet have a feedforward network, previous frames will not help in making the current decision.

I have read somewhere that Tesla uses two last frames captured to make a decision, even then 2 frames will not be that useful in this case. 
This might not be very helpful when predicting things ie.. lane cut-ins, as the system needs to observe the vehicle (that is going to cut in) behavior such as turn indicator, vehicle veering towards center lane over time in order to predict.

Can someone explain if this is the way production self-driving card such as Tesla work?



Or Is it something like the below?



Or are they using something like Many to one Recurrent net where inputs are CNN vectors of previous few frames and output is the control?
It varies quite substantially between different self-driving paradigms(rather obviously) but for the most part the vast majority of implementations are using a variety of different reference frames in order to make predictions.

For example, Tesla's Autopilot is being fed many different camera feeds as well as radar and ultrasonic signals that are processed in a variety of temporal contexts.

While, for the most part, all of these programs are very tight-lipped, we can make a variety of assumptions based on the information available and educated assumptions.

As with many large, complex ML/AI systems, there is a large amount of compartmentalization where many different connectionist(or sometimes classic) models are combined(à la youtube recommendation system). Tesla is likely utilizing recurrent and convolutional networks where particular modules(combinations of models) are deciding on specific contexts(temporal or signal-based). These outputs are then most likely fed into an actor network which makes real time decisions.