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comma Controls Challenge v2 is a project focused on developing machine learning models for low-level car controls. The goal is to write a controller that can effectively drive a simulated car along a given trajectory. The challenge utilizes a synthetic dataset based on the comma-steering-control dataset, derived from real car and road states from openpilot users. Participants need to implement a controller and evaluate its performance based on lateral acceleration cost and jerk cost. Competitive scores can lead to inclusion on the leaderboard. The project involves using Python, with specific packages to be installed. The TinyPhysics model simulates car dynamics based on inputs like velocity, acceleration, and steering. Evaluation is conducted by comparing a custom controller against a baseline (e.g., PID controller).

<h1>comma Controls Challenge v2</h1>
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<p>Machine learning models can drive cars, paint beautiful pictures and write passable rap. But they famously suck at doing low level controls. Your goal is to write a good controller. This repo contains a model that simulates the lateral movement of a car, given steering commands. The goal is to drive this "car" well for a given desired trajectory.</p>
<h2>Getting Started</h2>
<p>We'll be using a synthetic dataset based on the <a href="https://github.com/commaai/comma-steering-control">comma-steering-control</a> dataset for this challenge. These are actual car and road states from <a href="https://github.com/commaai/openpilot">openpilot</a> users.</p>
<pre><code class="language-bash"># install required packages
# recommended python==3.11
pip install -r requirements.txt

# test this works
python tinyphysics.py --model_path ./models/tinyphysics.onnx --data_path ./data/00000.csv --debug --controller pid
</code></pre>
<p>There are some other scripts to help you get aggregate metrics:</p>
<pre><code class="language-bash"># batch Metrics of a controller on lots of routes
python tinyphysics.py --model_path ./models/tinyphysics.onnx --data_path ./data --num_segs 100 --controller pid

# generate a report comparing two controllers
python eval.py --model_path ./models/tinyphysics.onnx --data_path ./data --num_segs 100 --test_controller pid --baseline_controller zero
</code></pre>
<p>You can also use the notebook at <a href="https://github.com/commaai/controls_challenge/blob/master/experiment.ipynb"><code>experiment.ipynb</code></a> for exploration.</p>
<h2>TinyPhysics</h2>
<p>This is a "simulated car" that has been trained to mimic a very simple physics model (bicycle model) based simulator, given realistic driving noise. It is an autoregressive model similar to <a href="https://blog.comma.ai/096release/#ml-controls-sim">ML Controls Sim</a> in architecture. Its inputs are the car velocity (<code>v_ego</code>), forward acceleration (<code>a_ego</code>), lateral acceleration due to road roll (<code>road_lataccel</code>), current car lateral acceleration (<code>current_lataccel</code>), and a steer input (<code>steer_action</code>), then it predicts the resultant lateral acceleration of the car.</p>
<h2>Controllers</h2>
<p>Your controller should implement a new <a href="https://github.com/commaai/controls_challenge/tree/master/controllers">controller</a>. This controller can be passed as an arg to run in-loop in the simulator to autoregressively predict the car's response.</p>
<h2>Evaluation</h2>
<p>Each rollout will result in 2 costs:</p>
<ul>
<li>
<p><code>lataccel_cost</code>: $\dfrac{\Sigma(\mathrm{actual_lat_accel} - \mathrm{target_lat_accel})^2}{steps} * 100$</p>
</li>
<li>
<p><code>jerk_cost</code>: $\dfrac{\Sigma((\mathrm{actual_lat_accel}<em>t - \mathrm{actual_lat_accel}</em>{t-1}) / \Delta t)^2}{steps - 1} * 100$</p>
</li>
</ul>
<p>It is important to minimize both costs. <code>total_cost</code>: $(\mathrm{lat_accel_cost} * 50) + \mathrm{jerk_cost}$</p>
<h2>Submission</h2>
<p>Run the following command, then submit <code>report.html</code> and your code to <a href="https://forms.gle/US88Hg7UR6bBuW3BA" rel="nofollow">this form</a>.</p>
<p>Competitive scores (<code>total_cost&lt;100</code>) will be added to the leaderboard</p>
<pre><code class="language-bash">python eval.py --model_path ./models/tinyphysics.onnx --data_path ./data --num_segs 5000 --test_controller &lt;insert your controller name&gt; --baseline_controller pid
</code></pre>
<h2>Changelog</h2>
<ul>
<li>With <a href="https://github.com/commaai/controls_challenge/commit/fdafbc64868b70d6ec9c305ab5b52ec501ea4e4f">this commit</a> we made the simulator more robust to outlier actions and changed the cost landscape to incentivize more aggressive and interesting solutions.</li>
<li>With <a href="https://github.com/commaai/controls_challenge/commit/4282a06183c10d2f593fc891b6bc7a0859264e88">this commit</a> we fixed a bug that caused the simulator model to be initialized wrong.</li>
</ul>
<h2>Work at comma</h2>
<p>Like this sort of stuff? You might want to work at comma! <a href="https://comma.ai/jobs">comma.ai/jobs</a></p>

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