Important flags

The following flags can be set to enable visualization of the output and state of the different Pylot components. The visualization is done in a pygame window, and users can switch between different views by pressing n.

  • --visualize_rgb_camera: Enables the visualization of the camera.
  • --visualize_depth_camera: Enables the visualization of the depth estimation.
  • --visualize_lidar: Enables top down visualization of the LiDAR.
  • --visualize_detected_obstacles: Enables visualization of detected obstacles.
  • --visualize_detected_traffic_lights: Enables visualization of detected traffic lights.
  • --visualize_detected_lanes: Enables visualization of detected lanes.
  • --visualize_tracked_obstacles: Enables the visualization of tracked obstacles. The visualization includes info such as: id of the obstacle, distance from ego vehicle, label.
  • --visualize_segmentation: Enables the visualization of segmented frames.
  • --visualize_waypoints: Enables the visualization of the waypoints output by the planning component. These waypoints can be drawn on the camera frame (pass -draw_waypoints_on_camera_frames), or directly in the simulator when running in simulation mode (pass --draw_waypoints_on_world).
  • --visualize_world: Enables visualization of the current state of the ego-vehicle. This is the best way to visualize what the self-driving car is currently perceiving and predicting. This visualization includes the past trajectories and predicted future trajectories of other agents, detected traffic lights and lanes, and the waypoints the ego-vehicle is trying to follow.

Flags that only work when running in simulation:

  • --visualize_imu: Enables visualization of the IMU.
  • --visualize_pose: Enables the visualization of the ego-vehicle pose.
  • --visualize_prediction: Enables the visualization of obstacle predictions.


Obstacle detection:


Traffic light detection:


Lane detection:


Planning waypoints:


Planning world:

In this visualization the ego-vehicle is driving forward at 9.0 meters per second, the vehicle on the opposite lane is stationary, and the prediction component predicts that the predestrian will cross the street.