I spent the last two months working from Saint Petersburg in Russia where I had the opportunity to engage with a great community of Python and Data Science engineers. I was greatly surprised by the rich social and networking activities they organize in a weekly basis: from conferences, formal talks, and global meetups with a few hundred attendees, to more informal drinkups and breakfast meetups.
This blog post is a summary of my talk about ‘Trends in Auto Tech’ at one of those events. You can find the slides and video at the end.
Problem and emerging solutions
Road traffic crashes are the leading cause of death among 15-29 years old population. Even though we have seen a lot of improvements on safety since the first car was manufactured 100+ years ago, there is still a lot of work ahead to reduce the more than 3,000 current deaths per day.
Recently, more computer power has accelerated the use of artificial neural networks. This has led to software programming getting seriously into the car industry in two forms: 1) Semi-Autonomous Vehicles or Assisted Driver Assistance Systems (ADAS), and 2) Fully Autonomous Vehicles. ADAS are the technologies based on camera and sensors that help the driver in the driving process. An example of this is Tesla Autopilot. The autopilot capability is able to keep the car on the track, break, accelerate and steer the wheel as appropriate, however it requires the driver to be in control all the time in the case is needed. Autonomous is when the car is able to drive without a driver. It’s a full driverless car. This means the largest transition ever in the car industry for both business and adoption perspectives (read 3 Ways To Speed Up The Adoption Of Autonomous Vehicles).
In the past couple of decades, the software development has been disrupting few industries. The Internet, Mobile, and Video Streaming have changed completely retail, banking, telecom, transportation, and entertainment businesses. Customers just love the easy to use, rapidly evolving, consistency and seamless experience across devices.
The car industry has remained vertical in the past, where just car manufacturers and suppliers built the entire end-to-end experience, according to a study from McKinsey. Today, customers demand a more horizontal model with participation from tech companies and third-party creators. They want a car with features that get updated over-the-air every week, that can play a podcast that continues at home, and learns from users habits to reduce the number of interactions. Even car owners with expensive built-in navigation systems, use Google Maps or Waze on their iPhones while driving.
Cars could become soon phones with wheels, and most businesses and use cases around the car would change forever. This will be a great opportunity for startups able to blend software with great customers experience, and for looking-forward car designers to let them use the car’s resources safely.
Investors are looking into auto tech startups for their ability to use software to improve safety, convenience, and efficiency in cars.
In addition to connected vehicle/driving data, fleet telematics, vehicle-to-vehicle communication, and auto cybersecurity, the more promising area is assisted and autonomous driving. According to CBInsights, investments deals in auto tech private companies already counted for more than $4B in 2017 and keep growing, most of the localized in a few regions.
Some examples of these deals are NuTonomy, a robo-taxi service in Singapore, Drive.ai that creates AI software for autonomous vehicles using deep learning, and FiveAI that build software for urban mobility in public transport.
Stay tuned for the second part of this post to learn more about trends in auto tech technologies and use cases.