new pid tune + readme fixes

README.md

@@ -16,7 +16,6 @@
 
 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.
 
-
 ## Getting Started
 We'll be using a synthetic dataset based on the [comma-steering-control](https://github.com/commaai/comma-steering-control) dataset for this challenge. These are actual car and road states from [openpilot](https://github.com/commaai/openpilot) users.
 
@@ -26,10 +25,10 @@ We'll be using a synthetic dataset based on the [comma-steering-control](https:/
 pip install -r requirements.txt
 
 # test this works
-python tinyphysics.py --model_path ./models/tinyphysics.onnx --data_path ./data/00000.csv --debug --controller pid 
+python tinyphysics.py --model_path ./models/tinyphysics.onnx --data_path ./data/00000.csv --debug --controller pid
 ```
 
-There are some other scripts to help you get aggregate metrics: 
+There are some other scripts to help you get aggregate metrics:
 ```
 # 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
@@ -43,22 +42,22 @@ You can also use the notebook at [`experiment.ipynb`](https://github.com/commaai
 ## TinyPhysics
 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 [ML Controls Sim](https://blog.comma.ai/096release/#ml-controls-sim) in architecture. Its inputs are the car velocity (`v_ego`), forward acceleration (`a_ego`), lateral acceleration due to road roll (`road_lataccel`), current car lateral acceleration (`current_lataccel`), and a steer input (`steer_action`), then it predicts the resultant lateral acceleration of the car.
 
-
 ## Controllers
 Your controller should implement a new [controller](https://github.com/commaai/controls_challenge/tree/master/controllers). This controller can be passed as an arg to run in-loop in the simulator to autoregressively predict the car's response.
 
-
 ## Evaluation
 Each rollout will result in 2 costs:
-- `lataccel_cost`: $\dfrac{\Sigma(actual\\_lat\\_accel - target\\_lat\\_accel)^2}{steps} * 100$
+- `lataccel_cost`: $\dfrac{\Sigma(\mathrm{actual\_lat\_accel} - \mathrm{target\_lat\_accel})^2}{steps} * 100$
 
-- `jerk_cost`: $\dfrac{\Sigma((actual\\_lat\\_accel\_t - actual\\_lat\\_accel\_{t-1}) / \Delta t)^2}{steps - 1} * 100$
+- `jerk_cost`: $\dfrac{\Sigma((\mathrm{actual\_lat\_accel}_t - \mathrm{actual\_lat\_accel}_{t-1}) / \Delta t)^2}{steps - 1} * 100$
 
-It is important to minimize both costs. `total_cost`: $(lataccel\\_cost * 50) + jerk\\_cost$
+It is important to minimize both costs. `total_cost`: $(\mathrm{lat\_accel\_cost} * 50) + \mathrm{jerk\_cost}$
 
 ## Submission
 Run the following command, then submit `report.html` and your code to [this form](https://forms.gle/US88Hg7UR6bBuW3BA).
 
+Competitive scores (`total_cost<100`) will be added to the leaderboard
+
 ```
 python eval.py --model_path ./models/tinyphysics.onnx --data_path ./data --num_segs 5000 --test_controller <insert your controller name> --baseline_controller pid
 ```

controllers/pid.py

@@ -6,15 +6,15 @@ class Controller(BaseController):
   A simple PID controller
   """
   def __init__(self,):
-    self.p = 0.3
-    self.i = 0.05
-    self.d = -0.1
+    self.p = 0.195
+    self.i = 0.100
+    self.d = -0.053
     self.error_integral = 0
     self.prev_error = 0
 
   def update(self, target_lataccel, current_lataccel, state, future_plan):
-      error = (target_lataccel - current_lataccel)
-      self.error_integral += error
-      error_diff = error - self.prev_error
-      self.prev_error = error
-      return self.p * error + self.i * self.error_integral + self.d * error_diff
+    error = (target_lataccel - current_lataccel)
+    self.error_integral += error
+    error_diff = error - self.prev_error
+    self.prev_error = error
+    return self.p * error + self.i * self.error_integral + self.d * error_diff