Privately Owned Vehicle Work Group Meeting - 2024/11/18 - Slot 1

[m-zain-khawaja]

Agenda

Welcome to new contributors

An outreach effort and call for participation was carried out to encourage new contributors to participate in the Privately Owned Vehicle work group, and new members will have the opportunity to briefly introduce themselves

Work group training

@idorobotics will go through the work-group training to help all members get up to speed on best practices related to Autoware Work Groups

Attendees

@m-zain-khawaja (Senior Tech Lead at AWF and Privately Owned Vehicle Work Group Chair)
@idorobotics (Project Manager at AWF)
Narendar Selvaraj (Senior Member of Technical Staff at AMD)
Krishna Thallapalli (Technical Specialist at Hexagon Manufacturing Intelligence, India)

Discussion

• Introductions by attendees
• Run through of agenda items by @m-zain-khawaja
• Welcome to Krishna, a new joiner to the Privately Owned Vehicle Work Group
• A detailed presentation was shared by @idorobotics on work group best practices, this included
  • overview of current work groups in Autoware and their activities
  • how to join and participate in work groups
  • meeting scheduling
  • best practices for meeting minutes documentation and meeting minutes recording
  • communication channels for work group, including Discord, ROS Discourse, Github Projects, Working Group Wikis and Github Discussions
  • @idorobotics requested that the Privately Owned Vehicle work group wiki be created under a different page, and @m-zain-khawaja agreed to action this item
  • review of Autoware Discord and run through of various channels and their purpose
  • review of existing projects and examples of work groups following good project tracking practices, e.g. Simulation Work Group
  • review of Github discussions, and how to filter by labels
  • review of how new ideas and features have been suggested on Github
• @m-zain-khawaja then presented the latest work related to SuperDepth - a vision-based depth estimation system which is part of the AutoSeg Foundation Model, this included:
  • motivations behind creating SuperDepth as a robust all-obstacles detection system, serving as a core safety layer for autonomous vehicles in edge-case scenarios
  • overview of neural network architecture of SuperDepth and explanation of key components of the network architecture
  • overview of existing vision-based depth estimation methods including deep stereo-matching methods, zero-shot monocular methods, as well as their strengths and limitations
  • overview of proposed methodology for SuperDepth as a best of both worlds approach
  • overview of importance on robust prediction at depth boundaries between foreground/background scene elements for reliable downstream pointcloud processing
  • sharing of current progress in terms of neural network blocks that have been designed and coded-up, as well as progress in dataset curation, with the goal to complete dataset curation by next week and be able to begin training the network by mid-December, with the aim to have the fully trained network ready at the start of the New Year

Zoom Meeting Video Recording

Video Meeting Link
Please contact the work group lead (@m-zain-khawaja ) to request access to a recording of this meeting.