Breakout 11: Artificial Intelligence (AI) and Machine Learning (ML) for Automated Vehicles (AV): Exp

Breakout 11: Artificial Intelligence (AI) and Machine Learning (ML) for Automated Vehicles (AV): Exploring Tools, Algorithms, and Emerging Issues

Tuesday, July 11, 1:30 PM – 5:30 PM
Golden Gate 3

Organizers:

  • Sherif Ishak, Louisiana State University
  • Shawn Kimmel, Booz Allen Hamilton

Autonomous driving relies on in-vehicle computers that emulate the functions of a human brain in making informed decisions. Such systems employ artificial intelligence and sophisticated machine learning methods to support object tracking and various pattern recognition capabilities. This session will provide an overview of applications that utilize Artificial Intelligence and Machine Learning tools supporting critical autonomous vehicles functions, as well as highlight emerging issues and challenges to overcome with such advanced computing tools.

This breakout session will feature seven presentations, each limited to 20 minutes followed by 5 minutes for questions. There will be a 20 min break half way through the session and a 45 min interactive discussion with the speakers at the end of the session. Given that the theme of this breakout session is to highlight the crucial role of artificial intelligence and machine learning tools in supporting the technology and applications for vehicle automation, all presenters are required to state clearly in their presentation how their work is aligned with the theme of this session. In essence, the speakers must highlight the elements of artificial intelligence and machine learning in their work so that the audience can understand the relevance of this work to the mission and scope of ABJ70.

Moderator – Sherif Ishak, Louisiana State University

1:30 PM – 1:55 PM
A Real-Time Data-Driven Decision-Support Toolkit for the Incentivization and Guidance of Shared, Electrified, and Automated Vehicles (SEAVs)

  • Chenfeng Xiong, University of Maryland

1:55 PM – 2:20 PM
Coordinated Decentralized Optimal Control for Connected and Automated Vehicles

  • Andreas A. Malikopoulos, University of Delaware

2:20 PM – 2:45 PM
Seeing Safety: Deep Learning, Virtual Environments, and the Future of Autonomous Vehicles

  • Artur Filipowicz, Princeton University

2:45 PM – 3:10 PM
How Machine Learning and Swarm Intelligence Improve Efficiency of Connected & Automated Vehicles (CAV)

  • Xuewei Qi, University of California-Riverside

3:10 PM – 3:30 PM
BREAK

3:30 PM – 3:55 PM
A centralized and decentralized approach for incentive allocation as a part of smart mobility solutions

  • Mehrdad Shahabi, University of Michigan

3:55 PM – 4:20 PM
Seeing traffic signal bulb colors is not enough: predictive data for connected & autonomous driving

  • Thomas Bauer, Traffic Technology Services, Inc. Beaverton

4:20 PM – 4:45 PM
A Convolutional Neural Network Model for Detection of Road Features

  • Tim Wong, NVIDIA

4:45 PM – 5:30 PM
Discussion and closing remarks

Breakout 11 / 25