Accelerating Tech: the Future of Autonomous Vehicles

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The technologies driving forward a new era of autonomous vehicles have been accelerating exponentially in the past few years. The futuristic cars that until recently were only found in science fiction could be with with us sooner than you think, with the global connected car market size expected to reach $180 billion by 2022.

With more than half of the world’s population living in our cities, managing massive population growth is one of the most important development challenges of the 21st century. Today's 28 megacities of 10 million or more people will increase to 41 megacities by 2030, placing a massive strain on aging infrastructures. At the Machine Intelligence in Autonomous Vehicles Summit on 23-24 March, we'll explore how trends in machine learning, sensors, virtual assistants, vehicle-to-vehicle communications and more are impacting the development of tomorrow's transport. 

Bryan Mistele, President and CEO of INRIX, will present 'Data Driven: Connecting Cars for Smarter Cities' at the summit, sharing how breakthroughs in location technology, connectivity and big data are poised to transform urban mobility. I spoke to him to learn more.

What started your work in autonomous vehicles?
For more than a decade INRIX has been focused on improving transportation by connecting cars and cities. It’s our belief that society benefits if cities and drivers have better information to improve their decision making.  Connected cars can lead to safer, more efficient and less congested commutes.  With the advent of the autonomous vehicle, we see a whole new range of opportunities to improve transportation. There’s a school of thought that the autonomous vehicle is somehow a distinct and separate phenomenon from the connected car, but, in my mind, it is an extension. AVs take connected cars to the next logical step.  At INRIX, we’re excited about the potential cloud-based data, deep learning and AI can have to help improve the driving experience – with or without an actual driver behind the wheel.

What are the key factors that have enabled recent advancements in autonomous vehicles?
Cheaper sensors, more powerful on-board processing, refined algorithms and advances in machine learning have all been key to the advancement of AVs, but big data and self-learning are the lynchpins for bringing this technology to scale. Without the telematics data systems established during the connected car revolution, AVs would be limited to isolated operation rather than poised to deliver systematic improvements mobility.

What are the key challenges to progressing autonomous vehicles?
There are three primary challenges to the successful scaling of AVs:

1. Consumer trust and acceptance: 75% of American’s are “afraid” to ride in a self-driving car and 95% consumers feel the need to be able to take control of an autonomous vehicle. Without a coordinated effort by public and private stakeholders to build and preserve consumer trust, high-profile failures and misinformation will continue to limit the ability to bring this technology to scale.

2. A heavy-handed public sector: Lawmakers and regulators are excited by the prospect of AVs but remain largely uninformed about the technology and concerned about possible dangers to consumers. Without proactive efforts from the private sector to deploy AVs in a way that is transparent and data-driven, misguided regulation is likely to stifle innovation.

3. Data sharing: There is broad agreement that to realize the promise of AVs there will need to be a system for sharing information to improve safety and efficiency across operators. It is incumbent on the private sector to establish an industry-led framework for sharing or the public sector will mandate a platform for them.

How are you at INRIX currently using the cloud to build new driver experiences?
INRIX has been the leader in connected car services for more than a decade and we are constantly looking for ways to leverage our resources and expertise to improve mobility systems that are rapidly evolving. This includes delivering up-to-date parking availability, congestion levels and incidents to millions of vehicles around the world every day through our rich, cloud-based services.

What developments can we expect to see in autonomous vehicles in the next 5 years?
Over the next five years we’re going to see something everywhere and everything somewhere. We will see the incremental and limited automation of all new vehicles sold to consumers, while simultaneously seeing fully-autonomous vehicles deployed in a limited number of urban and suburban markets. Both sets of vehicles will feature suites of on-board sensors but will be built on a foundation of data and connectivity.

Outside of your own field, what area of machine learning advancements excites you most?
I’m most excited to see where machine learning will be leveraged to address healthcare needs. Much as in the mobility space, machine learning can help to make medicine cheaper, more effective and safer. At a time when rising healthcare costs are front and center for many around the world, these advancements will provide welcome relief.

Bryan Mistele will be speaking at the Machine Intelligence in Autonomous Vehicles Summit, taking place alongside the Machine Intelligence Summit, in San Francisco on 23-24 March. Meet with and learn from leading experts in autonomous vehicles, IoT, the smart dashboard, machine learning methods and predictive analytics. Register to attend here.

Confirmed speakers include Sam Kherat, Sr Manufacturing Automation Team Leader, Caterpillar; Gary Marcus Director, AI Labs at Uber; Teymur Sadikhov Senior, Vehicle Intelligence Engineer at Mercedes-Benz R&D; Luca Rigazio, Director of Engineering at Panasonic SV Lab; and Pratik Brahma, Machine Learning R&D at Audi/VW. View more speakers and topics here.

See the full events list here for summits and dinners focused on AI, Deep Learning and Machine Intelligence taking place in San Francisco, London, Amsterdam, San Francisco, Boston, New York, Singapore, Hong Kong, and Montreal!

Image source: UC Berkeley Original

Machine Learning Deep Learning AI Smart Transport Sensors Connected Car Smart Dashboard Autonomous Vehicles Machine Intelligence Summit Autonomous Vehicles Summit Machine Intelligence


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