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What’s The State Of AI In Transportation Today? 9 Experts Share Their Insights

  • 28 September 2019
  • Sam Mire

Cars are becoming more and more like a computer on wheels, and AI is playing a role in the automotive industry's forward-looking technology embrace. But what, exactly, does AI look like as a tool for modern transportation systems, vehicular or otherwise. These industry insiders have a first-hand view of AI's evolution in transportation. Here's what they had to say:

1. Tina Quigley, CEO of the Regional Transportation Commission of Southern Nevada (RTC)

“AI is leading the way in revolutionizing transportation. It is reshaping how we undertake traffic management in Southern Nevada by offering safer and more effective solutions to address, mitigate and prevent traffic congestion and incident response. It is because of AI we can realize traffic congestion early and modify traffic flow to reduce impact. We can understand where there are active work zones and report on such activity through navigation applications like Waze in an effort to reduce congestion by suggesting alternative routes to travelers. AI also plays a vital role in the progression of Autonomous Vehicles.”

2. Brendan P. Keegan, CEO of Merchants Fleet

“Today we’re seeing more vehicles offer more types of driver assistance technologies. Features like automatic emergency braking, facial recognition to detect driver fatigue, and lane departure warnings are becoming increasingly common. These types of radars and sensors are all technology that will enable us to put self-driving cars to use, but the development of software to tackle tasks like object and pedestrian detection – which is the more difficult task – is still in its early stages. The data being collected today will fuel an AI revolution.”

3. Juan Rodriguez, co-founder and CEO of FlashParking

“When it comes to parking, AI is just getting started. Only a handful of AI solutions are currently deployed. Many companies are still in the proof of concept phase, but it's coming fast.”



4. Bryce Johnstone, Automotive Segment Director at Imagination Technologies

The AI compute performance has now reached a point where TOPS/Sec, TFLOP/Sec and reasonable power budgets will enable autonomous driving. Whilst it’s a long way from being mainstream, it’s likely to be 2027, it’s a major step forward in engaging with the consumer to show the benefits of autonomous vehicles. From a practical perspective, 2019 is probably the year of the first Robotaxi deployment which effectively means Level 4 driving capability. It will be the first real interaction between unmanned vehicles and non-autonomous vehicles in relatively low-speed city environments with no backup driver.”

5. Alex Shartsis, founder and CEO of Perfect Price

“Nascent but growing. New entrants like Tesla, Uber and Lyft lead the way (or at least dominate the conversation) in using AI in transportation: Tesla uses it to automate driving while Uber and Lyft use it in supply/demand and revenue management. Few legacy companies are using AI in a way that is core to their business, though some are developing AI for image recognition in tolling (e.g. license plate readers, technology to identify the occupancy of a vehicle, etc.) Some more innovative rental car companies are using AI in pricing to balance demand and supply. Airlines, who have been early adopters of AI for everything from autopilot to ticket sales, are increasingly expanding their use of new and innovative AI technologies as margins become tighter.”

6. John Barrus, Director of Business Development at Groq

New ML accelerator hardware is coming with faster and higher performance allowing better object recognition and understanding for real-time control. One key issue is the speed of response, also called “latency”. For safety-critical decision-making, getting answers in less than a millisecond is essential.”


7. Hongmo Je, CTO of StradVision

In relation to the self-driving car sector, we are noticing two different models of AI development, especially on data collection and maturing AI through the gathered data. The first model is a centralized model where multiple companies and services unify under one AI network. In this model, since AI the network is shared and developed simultaneously throughout various parties, collecting data is much faster and diverse through the “ecosystem” – this is a nice model because, through this ecosystem, various business models can be tested in conjunction with the AI network.

The second model is where AI networks are developed through individual companies – this model makes not only the market but also AI networks novel and competitive. This model obviously is not as cooperative compared to its counterpart, but we are seeing a trend of various companies forming superficial alliances between various carmakers and car parts suppliers. But, at the end of the day, only superficial matters are dealt with and nothing fundamental.”

8. Matt Hill, Chief Science Officer at Rekor Systems

“AI impacts the transportation industry in multiple ways – including public safety, autonomous vehicles, traffic management, electronic tolling, and pedestrian safety. Smart cities are beginning to share big data from public transport in urban areas to track crime data in real-time. Automotive manufacturers are testing self-driving vehicles aimed at reducing accidents and increasing productivity. Cities, States, and toll operators as using AI to process vehicle license plates and vehicle characteristics for more accurate billing practices and deliver safer commutes for motorists. AI also predicts safe travel paths for pedestrians and cyclists, encouraging more diverse transportation methods.”

9. Joseph Notaro, Vice President, WW Automotive Strategy & Business Development, ON Semiconductor

AI finds a natural place in ADAS functions today. The ability to fuse data coming from 2 sensing modalities (i.e. Image + Radar) and then make a decision is already being implemented on many vehicles. Interior monitoring will also rely on AI.

Collecting data from different sensors inside the vehicle and then being able to classify passengers (child, adult, dog, cat), understand their state of health (drowsy driver, potential heart-attack), detect forgotten articles in the back seat are all functions that are being enabled by AI today.”

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About Sam Mire

Sam is a Market Research Analyst at Disruptor Daily. He's a trained journalist with experience in the field of disruptive technology. He’s versed in the impact that blockchain technology is having on industries of today, from healthcare to cannabis. He’s written extensively on the individuals and companies shaping the future of tech, working directly with many of them to advance their vision. Sam is known for writing work that brings value to industry professionals and the generally curious – as well as an occasional smile to the face.