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Conference: Transform X 2022
Authors: Laura Major
2022-10-19
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Motional's approach to developing autonomous vehicles involves continuous learning and data sharing across the industry.
- Motional uses a continuous learning framework to mine on-road driving data and discover rare scenarios or areas where there are challenges or issues with their performance.
- They up sample and incorporate more of these scenarios into their training data to improve their autonomy performance.
- Motional recognizes the need for richer development of data sets and sharing of those data sets to fuel the development across the industry.
- They have pioneered a data sharing culture that has now extended across the industry.
- Motional's approach involves not just increasing the volume of data, but getting the right data, including finding rare objects and identifying challenging scenarios.
- Their focus is on improving their autonomy performance to achieve true driverless capability.
Conference: Transform X 2022
Authors: Dragomir Anguelov, Marco Pavone, Alex Kendall, Kate Park
2022-10-19
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Experts discuss the challenges in incorporating machine learning into autonomous vehicles safely and effectively.
- Autonomous vehicles use multiple sensors to identify their surroundings, but face difficulties in identifying pedestrians, other vehicles, obstacles, and environmental conditions.
- Integrating complicated sensor suites, software, data management, and machine learning with engineering is a challenge.
- Collecting and labeling large amounts of data, integrating ML models with the rest of the self-driving stack, and improving the driver continuously are also challenges.
- Simulation plays a critical role in development.
- Different OEMs use unique approaches to leverage machine learning in their self-driving stack, with some using end-to-end learning and others preferring modular learning.
- Scaling to new environments quickly is a difficult challenge.
Conference: Transform X 2022
Authors: Austin Russell, Alexandr Wang
2022-10-19
Austin Russell, CEO and Founder of Luminar, will join Alexandr Wang, CEO and Founder of Scale for a fireside chat. The two will discuss the parallel missions of their respective companies with the purpose of training better models on high-quality, labeled data collected from the best 3D sensors available for autonomous systems.Wang will ask Russell about his journey from Stanford dropout to his company’s 2020 NASDAQ IPO, product development, and how he thinks about building the machine learning infrastructure to train better models on 3D point cloud data. Russell will explain why he believes high-quality 3D sensors are essential for safe autonomy, and why simple 2D imagery doesn’t suffice.Russell was a 2013 Thiel Fellow, which allowed him to drop out of his undergraduate studies and focus on Luminar full-time. He also became the world’s youngest billionaire as a result of his company’s IPO.trans
Conference: OpenAI + Data Forum 2022
Authors: Travis Addair, Nicolas Castet
2022-06-21
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The presentation discusses a full stack platform for optimizing data sharing and processing in a cluster environment.
- The platform uses the Hadoop Distributed File System (HDFS) and YARN for data storage and processing.
- The platform includes a peer-to-peer torrent-like protocol called Left-Right (Letbat) for efficient data transfer.
- The platform allows for easy sharing of data sets and includes a search function for finding relevant data.
- The platform is still in development and the team is looking for feedback and developers to contribute.
Conference: Transform X 2021
Authors: Austin Russell
2021-10-07
Austin Russell, CEO of Luminar Technologies, sits down with Alexandr Wang, CEO of Scale AI, to discuss the future impact of LIDAR sensors on the autonomous vehicle industry. Austin shares what he saw that was lacking in existing AV sensors that led him to create Luminar Technologies and dives into the core requirements that they set out to fulfill with their own LIDAR sensors. He explores the optimal way to balance the capabilities of different sensor technologies. Together Austin and Alexandr go on to discuss the bottlenecks inherent in learning from data at the scale required to build safe autonomous vehicles, how sensor manufacturers and OEM companies should be partnering together, and the business models and market opportunities that show the most promise in the future. What does the choice of sensor technologies mean for downstream perception, prediction, and planning algorithms? What do we need from today's hardware or software to enable L4/L5 urban self-driving autonomy? What is the most critical strategic decision that CEOs of companies in this space should be thinking about? Join this discussion to hear how LIDAR sensors for autonomous vehicles are moving from small-scale experimentation to large-scale production and what impact they might have in the industry and for vehicle-owners everywhere.
Conference: Transform X 2021
Authors: Jesse Levinson
2021-10-07
Jesse Levinson co-founded Zoox in 2014 and is the company's Chief Technical Officer (CTO). He joins Scale AI’s Head of Nucleus, Russell Kaplan, for a fireside chat to discuss the challenges of bringing a completely autonomous vehicle stack (vehicle platform + sensors) from the lab to real-world streets for autonomous ride-sharing vehicles. Together Jesse and Russell explore the biggest obstacles that need to be solved when making robot taxis. How do you train a model to generalize for the unknown? How can a model respond to new challenges safely or plan a new response in real-time? What are the limitations in perception for different sensor types? As you train a model to learn edge cases and scenarios, how do you prevent regression in other areas? Jesse shares how Zoox uses simulation to take advantage of real-world data to perform realistic testing at scale. Join this session to hear Zoox's insights on taking an entire end-to-end technology stack from experimentation to reality.