Autonomous vehicles (AVs) are expected to dramatically redefine the future of transportation. A key challenge researchers are solving is how to create models that are robust and reliable enough to predict the motion of traffic agents such as cars, cyclists, and pedestrians. These models are key in planning an AV's decisions. Even though there has been a surge of interest in this problem, there still isn't a clear winning technology for future motion prediction that satisfies all of the needs of AVs.
Learn how Lyft Level 5 is thinking about applying deep prediction and machine learning frameworks to create motion prediction models, and explore the challenges involved in building self-driving systems. After this webinar, you'll be able to apply what you've learned to our Prediction Dataset—the largest released to date.