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Releases: sigmaai/self-driving-golf-cart

v0.2.0

28 Dec 14:44
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v0.2.0 Pre-release
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Version 0.2.0 release.

Time for another release. Here are the major changes and improvements.

1. Segmentation Added

The semantic segmentation system has finally been added. It has its own msg type. The package is located in here

2. Autopilot

Combined steering and throttle prediction into one system. (to streamline computing). The new system can be found here

3. Simulation

Updated the package after the CARLA 0.9.2 release. Now can run the system with no issues.

4. Other small changes, mostly documentation improvements

v0.1.2

17 Sep 23:39
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v0.1.2 Pre-release
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0.1.2 Release Summary

  1. implemented object detection (tiny-yolo v2)
  2. added simulated camera

For the next release

  1. update detection to yolo v3
  2. integrated the simulated video feed to steering/acceleration system
  3. add segmentation to ROS
  4. add accelerator predictor & detection & seg to speed controlling module

Contact:

Neil @ [email protected]

v0.1.1

16 Apr 11:54
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v0.1.1 Pre-release
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Made some improvements to the visualization software. Major changes are:

  1. Object detection and visualization implemented
  2. Two kinds of steering visualization implemented
  3. Created an info screen for displaying useful stuff
  4. Implemented class activation for the steering model.

Developed and maintained: [email protected] | @NeilNie

v0.1.0 (First Release)

31 Mar 18:09
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Main changes:

  • added some debugging features
  • made the code safer, i.e. added error & exception handling
  • added a GUI for driving
  • added an image-based localization module --> have not implemented on hardware
  • other small stuff

Issues:

If you have questions, comments or issues related to the code or project, please open an Issue.

Contact:

Developer maintaining: Neil Nie [email protected]

0.0.1 pre-release

10 Mar 20:19
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After finishing phase 1 and phase 2.1 of the development process, we are ready to make our first release. Mostly completed components include:

  1. end-to-end deep learning steering predictions with different types of networks.
  2. semantic segmentation with ENet and CityScapes dataset
  3. dataset collection
  4. control the steering system electronically
  5. control the brakes & accelerator electronically