Self-driving vehicles represent a transformative shift in transportation. This technology promises to dramatically reduce injuries and fatalities, reduce traffic congestion, improve access to mobility for those who currently cannot drive due to age or disability, and open the doors to passenger economy. However, self-driving vehicles face significant challenges, including technological limitations, regulatory hurdles, and societal acceptance. This panel will explore the complexities of integrating self-driving technology into everyday transportation. It will examine why, despite advancements in aviation autopilot systems, self-driving vehicles are a more recent and challenging innovation. Key topics include whether self-driving cars need defensive driving rules akin to human drivers, and how they should handle urban environments where social interactions with pedestrians and cyclists are critical. Panelists will also address concerns about potential adversarial behaviors toward self-driving vehicles and the necessary infrastructure—both physical and digital—required to ensure the safe deployment of these technologies. The discussion will emphasize the importance of governance frameworks and city planning in ensuring that self-driving vehicles are adopted in a manner that benefits society while minimizing risks.
Marc Huber (BS General Motors Institute, MS and PhD University of Michigan) is a Staff Researcher at GM Research & Development, where he’s been the last 8 years working on applied research in the area of autonomous driving.
A Michigan native, he dislikes extreme weather, and overcoming the challenges it poses to self-driving vehicles has become his favorite research topic.
At GM, his work has touched on many aspects of autonomous driving solutions, including perception, sense-making, and particularly decision-making, with an emphasis on capturing and exploiting information beyond what perception can provide (so-called commonsense reasoning). This has involved exploring logic-based systems, cognitive architectures, ontologies, ‘medium’-language models (c.f. ConceptNet), and large language models.
Amir Khajepour is a professor of Mechanical and Mechatronics Engineering and the Director of Waterloo Center for Automotive Research (WatCAR) at the University of Waterloo.
He held the Tier 1 Canada Research Chair in Mechatronic Vehicle Systems from 2008 to 2022 and the Senior NSERC/General Motors Industrial Research Chair in Holistic Vehicle Control from 2017 to 2022.
His work has resulted in training of over 160 PhD and MASc students, 30 patents, over 600 research papers and books, numerous technology transfers, and several start-up companies.
He has been recognized with the Engineering Medal from Professional Engineering Ontario and is a fellow of the Engineering Institute of Canada, the American Society of Mechanical Engineering, and the Canadian Society of Mechanical Engineering.
Igor Gilitschenski is an Assistant Professor of Computer Science at the University of Toronto where he leads the Toronto Intelligent Systems Lab.
Previously, he was a (visiting) Research Scientist at the Toyota Research Institute.
He was also a Research Scientist at MIT’s Computer Science and Artificial Intelligence Lab and the Distributed Robotics Lab (DRL).
There he was the technical lead of DRL’s autonomous driving research team.
Dr. Gilitschenski joined MIT from the Autonomous Systems Lab of ETH Zurich where he worked on robotic perception, particularly localization and mapping.
He obtained his doctorate in Computer Science from the Karlsruhe Institute of Technology and a Diploma in Mathematics from the University of Stuttgart.
His research interests involve developing novel robotic perception and decision-making methods for challenging dynamic environments.
He is the recipient of several best paper awards including at the American Control Conference, the International Conference of Information Fusion, and the Robotics and Automation Letters.
Opportunities and Challenges of Smart Mobility in the MENA Region
Middle East and North Africa (MENA) is a diverse region with a large young population and rich natural and financial resources. In this region, there is a growing population shift from rural to urban areas driven by social and economic needs. This urbanization led to massive people and cargo mobility challenges and several negative impacts in cities related to road safety, congestion, and emissions. Several smart mobility technologies and business models are emerging to deal with these negative implications. These technologies include, but are not limited to green mobility, shared mobility, connected mobility, assisted and automated mobility, Mobility-as-a-Service (MaaS), micromobility and last-mile delivery. Several smart mobility services are enabled based on new business models such as sharing economy, gig economy and passenger economy. This panel discusses the opportunities and challenges of emerging smart mobility technologies and business models in the MENA region. The panel also addresses how government, city planners, and technologists can work together to respond to changes in people and cargo mobility systems and services in the MENA region.
Across the globe, there are tens of companies pursuing the development of automated driving systems (ADS), ranging over SAE Driving Automation Levels 3-5. There are several open engineering challenges of ADS software – particularly in development and validation of ADS operation in challenging weather conditions, critical corner cases, dealing with a variety of pedestrians and traffic conditions, and cooperation with human operated vehicles. Many standards (e.g., ISO 26262, ISO 21448) are emerging providing guidelines to ensure safety of ADS under the intended operating conditions.
On the other hand, there are societal concerns that technological advancements such as ADS and robotics might bring in, e.g., people being out of work, competing with automation etc.. Initiatives like Partners for Automated Vehicle Education (PAVE) aim to alleviate such concerns by educating public and policymakers on pros and cons of automated driving systems.
This panel discussion will focus on ADS engineering and societal adoption challenges and offer some possible solutions.
Senior Solution Leader, General Motors, Canada
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