feat(alpasim-integration): add alpasim driver plugin for closed-loop operation

[riita10069]

Summary

This PR implements the AlpaSim Driver Plugin (Model/plugins/alpasim_driver/) and the real-time Observation Stream Parser (Model/data_parsing/alpasim_stream/) to resolve #140.

It enables closed-loop evaluation and training rollouts of the AutoE2E policy within NVIDIA AlpaSim over the KitScenes 7-camera sensor topology at 10 Hz.

• AlpaSim Driver Plugin: Implements AutoE2EDriver (BaseTrajectoryModel subclass) and AutoE2EAlpaSimConfig, registering autoe2e under Python entry point groups alpasim.models and alpasim.configs.
• Stream Parser: AlpasimStreamParser preprocessor converting real-time PredictionInput payloads to normalized model observation tensors.
• Offline Parity Guarantee: parity with offline Model/data_parsing/kit_scenes/dataset.py.
• Standalone Examples & Tests: 50-step closed-loop simulation demonstration (examples/run_closed_loop.py), local smoke test (smoke_test_alpasim.py), and parity unit tests (test_alpasim_stream.py).

Architecture & Data Flow

AlpaSim Runtime
  ↓ PredictionInput (7 RGB camera frames, speed, acceleration, DriveCommand)
AutoE2EDriver (Model/plugins/alpasim_driver/plugin.py)
  ↓ AlpasimStreamParser (Model/data_parsing/alpasim_stream/parser.py)
AutoE2E Model Inference (normalized visual_tiles, egomotion_history)
  ↓ Waypoints [64, 2] + Headings [64]
ModelPrediction (trajectory_xy, headings in rig frame)
  ↓ AlpaSim Physics & Kinematic State Update

Registered Entry Points (pyproject.toml)

[project.entry-points."alpasim.models"]
autoe2e = "alpasim_autoe2e_plugin:AutoE2EDriver"

[project.entry-points."alpasim.configs"]
autoe2e = "alpasim_autoe2e_config:AutoE2EAlpaSimConfig"

Validation

• pytest, ruff, and mypy clean
• Coverage: 100% coverage on alpasim_stream/parser.py (49/49 statements), 84% total coverage across the plugin suite.
• Closed-Loop Rollout Verification: Executed 50 steps at 10 Hz (5.0s simulated, 76.72m traveled, mean policy latency 10.60 ms/step) using AlpaSim physics feedback. I did not load actual checkpoint weights, so this was done for dummy inputs.

Acceptance Criteria

• Model/data_parsing/alpasim_stream/parser.py is implemented.
• Live observations parsed from stream are dimensionally and typographically identical to offline KitScenes dataset outputs.
• Unit tests added to Model/tests/ verifying parser compliance against offline loader.
• Local smoke test run successfully generating world model visualization (smoke_test_alpasim.py).
• Standalone 50-step closed-loop simulation demonstration added (Model/plugins/alpasim_driver/examples/run_closed_loop.py).

Closes #140

@riita10069 @m-zain-khawaja

P.S. I also added Pillow to our requirements since it is used in the visualization