Tesla’s Full Self-Driving suite continues to improve with a recent video showing a Model 3 safely shifting away from a makeshift lane of construction cones while using Navigate on Autopilot.

Tesla owner-enthusiast Jeremy Greenlee was traveling through a highway construction zone in his Model 3. The zone contained a makeshift lane to the vehicle’s left that was made up of construction cones.

In an attempt to avoid the possibility of any collision with the cones from taking place, the vehicle utilized the driver-assist system and automatically shifted one lane to the right. This maneuver successfully removed any risk of coming into contact with the dense construction cones that were to the left of the car, which could have caused hundreds of dollars in cosmetic damage to the vehicle.

When opportunity knocks, open the door: No one has taken heed of that adage like Nvidia, which has transformed itself from a company focused on catering to the needs of video gamers to one at the heart of the artificial-intelligence revolution. In 2001, no one predicted that the same processor architecture developed to draw realistic explosions in 3D would be just the thing to power a renaissance in deep learning. But when Nvidia realized that academics were gobbling up its graphics cards, it responded, supporting researchers with the launch of the CUDA parallel computing software framework in 2006.

Since then, Nvidia has been a big player in the world of high-end embedded AI applications, where teams of highly trained (and paid) engineers have used its hardware for things like autonomous vehicles. Now the company claims to be making it easy for even hobbyists to use embedded machine learning, with its US $100 Jetson Nano dev kit, which was originally launched in early 2019 and rereleased this March with several upgrades. So, I set out to see just how easy it was: Could I, for example, quickly and cheaply make a camera that could recognize and track chosen objects?

Embedded machine learning is evolving rapidly. In April 2019, Hands On looked at Google’s Coral Dev AI board which incorporates the company’s Edge tensor processing unit (TPU), and in July 2019, IEEE Spectrum featured Adafruit’s software library, which lets even a handheld game device do simple speech recognition. The Jetson Nano is closer to the Coral Dev board: With its 128 parallel processing cores, like the Coral, it’s powerful enough to handle a real-time video feed, and both have Raspberry Pi–style 40-pin GPIO connectors for driving external hardware.

How can we train self-driving vehicles to have a deeper awareness of the world around them? Can computers learn from past experiences to recognize future patterns that can help them safely navigate new and unpredictable situations?

These are some of the questions researchers from the AgeLab at the MIT Center for Transportation and Logistics and the Toyota Collaborative Safety Research Center (CSRC) are trying to answer by sharing an innovative new open dataset called DriveSeg.

Through the release of DriveSeg, MIT and Toyota are working to advance research in autonomous driving systems that, much like human perception, perceive the driving environment as a continuous flow of visual information.