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G55 TCC adaptation field evidence

I captured this while investigating repeated same-gear RPM changes in my 2011 G55 AMG with an Ultimate-NAG52 V1.3 Rev B installed.

The attached CSV is a cleaned two-second excerpt from a 10 Hz NAG52 diagnostic log. Absolute timestamps, raw diagnostic payloads, unrelated channels, and vehicle identifiers were removed. Throughout the excerpt the transmission reports fifth gear and a closed TCC, accelerator position remains at 82 raw, reported engine torque remains about 150–155 Nm, and turbine/output speeds stay nearly constant. Engine-to-turbine slip rises from 7 RPM to 128 RPM as target TCC pressure reaches roughly 1070→1000 mbar.

The fresh fifth-gear A1 lock-pressure row read from the controller was:

Load (%)	0	10	20	30	40	50	60	70	80	90	100	125	150

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marp	true
theme	chaos
paginate	true
footer	LLM Sensitivity

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# Setup imports
import os
from pathlib import Path

import cv2
from matplotlib import pyplot as plt
import numpy as np
import onnx
import onnxruntime
import torch

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import random
import time

# Create lists of 100,000 random numbers each
random_list_1 = [random.randint(1,1e6) for i in range(int(1e5))]
random_list_2 = [random.randint(1,1e6) for i in range(int(1e5))]

start = time.time()
matching_numbers = 0
for number in random_list_1:

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    def learn(self, experiences, gamma):
        """ 
        Update value parameters using given batch of experience tuples

        Params
        ======
            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
            gamma (float): discount factor
        """
        states, actions, rewards, next_states, dones = experiences

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def sample(self):
    """Randomly sample a batch of experiences from memory"""
    experiences = random.sample(self.memory, k=self.batch_size)

    states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
    actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
    rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
    next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)
    dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)

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class QNetwork(nn.Module):
    """Actor (Policy) Model"""

    def __init__(self, state_size, action_size, seed, fc1_units=64, fc2_units=64):
        """
        Initialize parameters and build model

        Params
        ======
            state_size (int): Dimension of each state

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// define the (x,y) points we will use for the planner
vector<double> next_x_vals;
vector<double> next_y_vals;
// start with previous path points from above
for (int i = 0; i < prev_size; i++)
{
  next_x_vals.push_back(previous_path_x[i]);
  next_y_vals.push_back(previous_path_y[i]);
}
// calculate how to break up spline points to travel at desired reference velocity

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// create a spline
tk::spline s;
// set (x,y) points to the spline
s.set_points(ptsx, ptsy);

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if (car_ahead)
  {
    cout << "Car Ahead!!!\n\n\n\n\n\n"
       << endl;
    if (!car_left && lane > 0) // no left-car, yes left-lane
    {
      lane--; // Change lane left
    }
    else if (!car_right && lane != 2) // no right-car, yes right-lane
    {