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Searching for a Parking Spot? AI Got It
Anyone who’s circled a busy parking lot or city block knows that finding an open spot can be tricky. Faded line markings. Big trucks hiding smaller cars. Other drivers on … Read article >
Taking It to the Streets: Ride in an NVIDIA Self-Driving Car with DRIVE Labs
Turning a traditional car into an autonomous vehicle is an enormous challenge. At NVIDIA, we’re tackling the problem by building the essential blocks of autonomous driving — categorized into perception, … Read article >
Read Between the Lines: How We Taught Neural Nets to Predict Lane Lines
Lane markings are critical guides for autonomous vehicles, providing vital context for where they are and where they’re going. That’s why detecting them with pixel-level precision is fundamentally important for self-driving cars.
To Go the Distance, We Built Systems That Could Better Perceive It
Simple rule: If you can’t judge distances you shouldn’t drive. The problem: judging distances is anything but simple. We humans, of course, have two high-resolution, highly synchronized visual sensors — our eyes — that let us to gauge distances using stereo-vision processing in our brain.
DRIVE Labs: Predicting the Future with RNNs
Autonomous vehicles must use computational methods and sensor data, such as a sequence of images, to figure out how an object is moving in time.
DRIVE Labs: Pursuing Perfection for Intersection Detection
Navigating a traffic-light controlled intersection may seem routine. But when the NVIDIA BB8 autonomous test vehicle first performed that task last year, it had our engineers smiling.
DRIVE Labs: How We’re Building Path Perception for Autonomous Vehicles
Having confidence in a self-driving car’s ability to use data to perceive and choose the correct drivable path while the car is driving is critical. We call this path perception confidence.