Overcoming the Most Difficult Challenges in Autonomous Vehicles

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.

During the presentation, the panelists discussed the difficulty autonomous vehicles face in identifying aggressive drivers. This is a challenge because aggressive drivers may not follow traffic laws or signals, making it difficult for the autonomous vehicle to predict their behavior and respond accordingly.

Experts working on autonomous vehicles will discuss the rapid pace of research and innovation in machine learning, highlighting the most exciting developments over the last few years and how they approach incorporating the constant advances into an autonomous vehicle safely. The panelists are industry leaders working at NVIDIA, Tesla, Waymo, and Wayve. They will also discuss the unique approaches each OEM takes to leverage machine learning in its self-driving stack, with some using end-to-end learning and others preferring modular learning, and each method's advantages and disadvantages. They will also discuss best practices for integrating complicated sensor suites, software, data management, and machine learning with engineering. Other challenges they will cover include collecting large amounts of data, managing, and labeling datasets, integrating ML models with the rest of the self-driving stack, and how to improve the driver continuously. They will also discuss the critical role of simulation in development, and the current state of self-driving cars and the most difficult challenges they face today, including scaling to new environments quickly.