feat(alpasim-integration): continuation of closed loop integration

[Arseni10Lk]

This is a PR for PR #175 .

PR #166 was closed automatically due to a history revamp, and now this PR continues the work on the closed-loop integration.

Acceptance order:

• First, this PR has to be accepted to amend PR feat(alpasim-integration): add alpasim driver plugin for closed-loop operation #175.
• After this, PR feat(alpasim-integration): add alpasim driver plugin for closed-loop operation #175 can be accepted

[Arseni10Lk]

@riita10069, thanks for all your feedback. I really appreciate all the great ideas shared

So, I examined the navigation and its potential implementation here, and I wanted to confirm/discuss one thing.

Constructing a route mask shouldn't be an issue (at least not as much as map rasterization). However, when it comes to map rasterization, there is one critical difference from offline training that requires some thought.

• As far as I understand, in production, the maps are rasterized before the run (so not on the fly, meaning the model does not have to "wait" for the map to be available). This is possible because it is IL and we have an actual trajectory, so rendering in advance is not an issue.
• Here, however, rendering in advance is impossible (well, if we want it to be precise, at least). So we can only rasterize a map on the fly, which might become a bottleneck.

So, the key question I wanted to ask is whether you had a chance to test live rasterization before, and how much it slows down performance in combination with a 10 Hz frequency of operation of our Reactive model

[Arseni10Lk]

I think this actually can be resolved by simply pulling a "bigger" tile than needed first and then just cropping (not literally cropping, but rather rasterizing its chunk) it in accordance with the current position. So, now I will be working on that

[riita10069]

I think one important point is how much freedom we actually want to give the model during RL.

Since the 3DGS environment is reconstructed from camera data along the original vehicle trajectory, the agent cannot deviate too far from it. For example, if it moves more than ~3 m away, we could give a negative reward and terminate the episode, since we probably shouldn’t train on clearly degraded regions of the reconstruction.

So even with RL, the task is essentially to follow the same route as the original vehicle, while allowing some local freedom in how the agent drives.

Given this constraint, I think we can preprocess and pack a sufficiently large rasterized map and route with each scene, similar to IL. During RL, we would only need to sample the relevant region based on the agent’s current pose, rather than rasterizing the map from scratch at every step.

@Arseni10Lk cc. @m-zain-khawaja