It's dark and raining, plus the traffic is relatively heavy at times.
Tesla owner and popular YouTube influencer Andy Slye takes us along for a ride in his Tesla Model 3 during his usual commute. He aims to show us how Tesla Autopilot performs. In addition, he tests out the system's newest feature. It pretty fascinating to see the technology in action, and Slye helps out by narrating.
Tesla Autopilot is continuously improving. This is because Tesla monitors it in all of its vehicles and then uses over-the-air software updates to push improvements to its fleet. With any software, sometimes updates can lead to issues, but those issues can then be resolved with subsequent updates.
While Tesla Autopilot is still not even close to being fully autonomous, and Tesla's Full Self-Driving Capability isn't feature-complete, the technology is capable of assisting with many typical driving tasks. Just recently, Tesla updated the system with the ability to stop for traffic lights and stop signs. The feature requires driver confirmation for now as it learns through experience and data collection.
Check out Slye's Tesla Autopilot commute. His drive takes 45 minutes, but he has sped up the video to make it shorter. As you can see, the driving is in the dark, in the rain, and in relatively heavy traffic. Then, let us know what you think in our comment section below. We'd also love to hear about your Autopilot experiences.
Video Description via Andy Slye on YouTube:
Tesla Autopilot Drives Itself on My 45-Minute Commute in 2020
Here's how my Tesla Model 3 (HW3 + Enhanced Autopilot & Full Self-Driving) drives in the dark and rain on my daily commute in 2020. We also take a look at the latest Tesla software update that added automatic stopping at stop lights and stop signs to see how it performs in real life.
Tesla develops and deploys autonomy at scale, and they believe that an approach based on advanced AI for vision and planning, supported by efficient use of inference hardware is the only way to achieve a general solution to full self-driving.
Build silicon chips that power our full self-driving software from the ground up, taking every small architectural and micro-architectural improvement into account while pushing hard to squeeze maximum silicon performance-per-watt. Perform floor-planning, timing and power analyses on the design. Write robust, randomized tests and scoreboards to verify functionality and performance. Implement compilers and drivers to program and communicate with the chip, with a strong focus on performance optimization and power savings. Finally, validate the silicon chip and bring it to mass production.
Apply cutting-edge research to train deep neural networks on problems ranging from perception to control. Our per-camera networks analyze raw images to perform semantic segmentation, object detection and monocular depth estimation. Our birds-eye-view networks take video from all cameras to output the road layout, static infrastructure and 3D objects directly in the top-down view. Our networks learn from the most complicated and diverse scenarios in the world, iteratively sourced from our fleet of nearly 1M vehicles in real time. A full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hours to train. Together, they output 1,000 distinct tensors (predictions) at each time step.
Develop the core algorithms that drive the car by creating a high-fidelity representation of the world and planning trajectories in that space. In order to train the neural networks to predict such representations, algorithmically create accurate and large-scale ground truth data by combining information from the car's sensors across space and time. Use state-of-the-art techniques to build a robust planning and decision-making system that operates in complicated real-world situations under uncertainty. Evaluate your algorithms at the scale of the entire Tesla fleet.
Throughput, latency, correctness and determinism are the main metrics we optimize our code for. Build the Autopilot software foundations up from the lowest levels of the stack, tightly integrating with our custom hardware. Implement super-reliable boot loaders with support for over-the-air updates and bring up customized Linux kernels. Write fast, memory-efficient low-level code to capture high-frequency, high-volume data from our sensors, and to share it with multiple consumer processes— without impacting central memory access latency or starving critical functional code from CPU cycles. Squeeze and pipeline compute across a variety of hardware processing units, distributed across multiple system-on-chips.
Build open- and closed-loop, hardware-in-the-loop evaluation tools and infrastructure at scale, to accelerate the pace of innovation, track performance improvements and prevent regressions. Leverage anonymized characteristic clips from our fleet and integrate them into large suites of test cases. Write code simulating our real-world environment, producing highly realistic graphics and other sensor data that feed our Autopilot software for live debugging or automated testing.