THE ENTIRE INDUSTRY
AT YOUR FINGERTIPS.
At the world’s largest (virtual) event for unmanned and autonomous systems, you’ll find your momentum, that something extra that gives you a competitive edge – your X factor.

Only AUVSI XPONENTIAL 2020 allows you to discover groundbreaking innovation, immerse yourself in new ideas, gain new perspectives and experience everything you need to elevate your business from the comfort of your own home.
TOUR THE PLATFORM
Defense Energy Agriculture Retail + Logistics Public Safety Transportation Communications Mapping + Surveying Oil + Gas Construction Automated Vehicles Mining
From energy to transportation and construction to defense, join the unmanned systems community including users, technologists and policymakers to collaborate on ideas, share lessons learned and build new partnerships. SEE WHO ATTENDS
FIND YOUR EDGE
Explore today’s proven capabilities and tomorrow’s advancements through virtual exhibits, community Q&A, and roundtable discussions with hundreds of manufacturers and service providers. SEE WHO'S EXHIBITING
FIND YOUR INSPIRATION
Get practical solutions and easily implementable ideas you can put into action right away through live-streamed and on-demand educational sessions covering the industry’s most pressing topics.
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Welcome to your XPONENTIAL Exhibitor Console!

The Exhibitor Console is your XPONENTIAL hub for all the information you need to know – from deadlines to promotional items - in one convenient location.

Important Dates and Information

Virtual Platform Launch

Note: only registered attendees can access the virtual platform and virtual booth. 

September 28, 2020

Virtual Platform Coffee Talk

  • Zoom Link 
  • Meeting ID: 865 9877 2303
  • Passcode: 447699
  • Phone: +1 301 715 8592

October 1, 2020

1:00pm EST

XPONENTIAL 2021 Rebook

Rebook will take place during XPONENTIAL 2020. You will receive an email by Thursday, October 5 with your rebook time. This will be the time that you can log into the system and select your physical booth location for XPONENTIAL 2021 scheduled for Atlanta May 3-6.

October 7-8, 2020

Use our Promotional Toolkit to share your participation at XPONENTIAL and give your customers a discounted registration with code EXHOFFXPO20!

Beyond Teleoperation: Towards Explainable and Trusted Autonomous Vehicles

  • Session Number:2052
Tuesday, October 06, 2020: 10:30 AM - 11:15 AM

Speaker(s)

Session Speaker
Jeff Druce
Senior Scientist
Charles River Analytics

Description

Machine Learning has made tremendous strides in the past decade, showing promise in many areas of active research, as well as pragmatic application in a variety of industries, and even finance. In particular, deep neural networks (DNNs) have been shown to perform extremely well in a variety of computer vision tasks such as classification and segmentation. Although DNN based tools currently can assist operators of unmanned vehicles, these cutting edge algorithms have not yet been accepted as trusted partners in critical tasks, such as control systems for these autonomous vehicles. For ML systems to act as trusted partners when performing critical tasks, a comprehensive understanding of the system’s competencies is necessary, as well as an understanding of how their input is mapped to an output. However, it is challenging to accurately understand and predict an ML system’s performance. Even state-of-the-art ML algorithms can be sensitive to changes in their environment—they can perform poorly when there are just subtle deviations from training, and may do so with no awareness of their poor performance. A framework that transforms autonomous systems from tools into trusted, collaborative partners, allowing human operators to gain insight into a system’s competence in complex environments. Our approach for developing his framework first identifies and catalogs the experiences of ML systems via mathematically constrained distilled representations of their internal processes. These representations surface human-intelligible features that are meaningful to both the ML system and to the user. Second, our framework learns rich probabilistic causal models with a relational structure that can describe the dependencies that ultimately determine the ML system’s task behaviors. These models are powerful because they can still function when data is fragmented or missing and can provide informative counterfactual queries to help the user explore similar scenarios to determine the ML system’s likely competency. Finally, our framework provides an intuitive interface that provides users with rich comprehensive measures of system performance, recommendations of when to adjust the ML system to perform better for the selected scenario, and what-if scenarios in which the selected ML system would perform better. This approach will yield an efficient ML system that is aware of its own competencies and can provide the information the human partner needs to make informed decisions about scenarios in which the ML system can be trusted.

Super Tracks:


Job Role:
Analyst,Engineering/Technical,Research & Development

Tags:
Artificial Intelligence,Ground



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