Diskussion:Controlled Autonomous Driving for a JetRacer: Unterschied zwischen den Versionen

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Die Seite wurde neu angelegt: „ {| class="wikitable" |- ! # !! date !! plan for this week !! progress |- | 4 || 31.03.26 || * Is heading from camera a good solution? * Compare Heading from cam, gyro, TopCon and map in one plot. * Decide what is the best. * Speed um the car. * Extend propagation distance to 1,5 m. * Focus on lane following instead of lidar. * heading: 0° = north, 90° = east, 180° = south, 270° = west * In our map the first straigth leads the car to th…“
 
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= Concept=
1) Per-frame camera processing (lower rate)
* Extract lane/track features (lane lines, road edges, path points).
* Estimate curvature κ_cam(s) at a chosen look-ahead distance s (or a small set of look-ahead distances).
2) High-rate IMU loop
* Read yaw rate r, steering angle δ (if available), wheel speeds.
* Use a simple kinematic/dynamic model (e.g. bicycle model) to predict short-term motion and where the vehicle will be in 1–2 m given current yaw rate and speed.
3) State estimation / sensor fusion
*Run an EKF/UKF or Kalman filter with states like lateral error, heading error, yaw rate, possibly curvature.
*Inputs/measurements: IMU yaw rate, steering angle, odometry; camera curvature or look-ahead heading as a lower-rate measurement/observation.
*Let the IMU provide high-frequency propagation and the camera provide periodic corrections (adapt measurement covariances when camera quality is low).
4) Compute steering command (feedforward + feedback)
*Feedforward: compute required steering angle <math>δ_{ff}</math> from κ_cam and vehicle speed v (use steering geometry/bicycle model).
*Feedback: compute corrective term from state errors <math>δ_{fb}</math> (e.&thinsp;g. lateral error, heading error, yaw-rate error) using a controller (PID on yaw-rate or lateral error, LQR, or state-feedback).
* Final command: <math>δ = δ_{ff} + δ_{fb}</math>, then apply actuator limits (max steering angle, rate limits).
Latency compensation & look-ahead tuning
Compensate for camera processing delay by propagating the estimated state forward by the measured latency before using camera-derived references.
Choose look-ahead distance dependent on speed: higher speed → longer look-ahead; lower speed → shorter.
Dynamically weight camera vs IMU in the estimator based on confidence (visibility, image quality).

Version vom 6. April 2026, 11:30 Uhr

# date plan for this week progress
4 31.03.26
  • Is heading from camera a good solution?
  • Compare Heading from cam, gyro, TopCon and map in one plot.
  • Decide what is the best.
  • Speed um the car.
  • Extend propagation distance to 1,5 m.
  • Focus on lane following instead of lidar.
  • heading: 0° = north, 90° = east, 180° = south, 270° = west
  • In our map the first straigth leads the car to the east, than north ...
  • New scenario: AMR starts in left lane and stears automaticly in the middle of the right lane.
  • Compare different controller for this task: Pure Pursuit, Stanley Controller, Linear-Quadratic Regulator (LQR), Model Predictive Control (MPC),...

Concept

1) Per-frame camera processing (lower rate)

  • Extract lane/track features (lane lines, road edges, path points).
  • Estimate curvature κ_cam(s) at a chosen look-ahead distance s (or a small set of look-ahead distances).

2) High-rate IMU loop

  • Read yaw rate r, steering angle δ (if available), wheel speeds.
  • Use a simple kinematic/dynamic model (e.g. bicycle model) to predict short-term motion and where the vehicle will be in 1–2 m given current yaw rate and speed.

3) State estimation / sensor fusion

  • Run an EKF/UKF or Kalman filter with states like lateral error, heading error, yaw rate, possibly curvature.
  • Inputs/measurements: IMU yaw rate, steering angle, odometry; camera curvature or look-ahead heading as a lower-rate measurement/observation.
  • Let the IMU provide high-frequency propagation and the camera provide periodic corrections (adapt measurement covariances when camera quality is low).

4) Compute steering command (feedforward + feedback)

  • Feedforward: compute required steering angle Fehler beim Parsen (Syntaxfehler): {\displaystyle δ_{ff}} from κ_cam and vehicle speed v (use steering geometry/bicycle model).
  • Feedback: compute corrective term from state errors Fehler beim Parsen (Syntaxfehler): {\displaystyle δ_{fb}} (e. g. lateral error, heading error, yaw-rate error) using a controller (PID on yaw-rate or lateral error, LQR, or state-feedback).
  • Final command: Fehler beim Parsen (Syntaxfehler): {\displaystyle δ = δ_{ff} + δ_{fb}} , then apply actuator limits (max steering angle, rate limits).


Latency compensation & look-ahead tuning


Compensate for camera processing delay by propagating the estimated state forward by the measured latency before using camera-derived references. Choose look-ahead distance dependent on speed: higher speed → longer look-ahead; lower speed → shorter. Dynamically weight camera vs IMU in the estimator based on confidence (visibility, image quality).

Abgerufen von „https://wiki.hshl.de/wiki/index.php?title=Diskussion:Controlled_Autonomous_Driving_for_a_JetRacer&oldid=147676“