Lyft CEO: we’re setting a multi-sensor safety standard for autonomous rides

Every day, millions of rides happen on the Lyft platform. These rides hold your most precious cargo — family, friends, and colleagues — which is why we are committed to being the safest way to get around. There are approximately 40,000 deaths and 2.4 million injuries in the U.S. every year on the roads. Car crashes are the leading cause of death for teens and young adults, and remain among the leading causes of death across much of the lifespan. We accept these numbers in a way we would never tolerate from any other form of transportation. Speeding, alcohol impairment and distracted driving are the largest contributors. We want to make that number zero, and Autonomous Vehicles (AVs) are part of that plan. AVs never drive distracted and are designed to obey speed limits and traffic laws. But not all AV technology is created equal. Sensors are the eyes and ears of a self-driving system, and providers have taken sharply divergent approaches. As we’ve evaluated various systems, we’ve come to a conclusion: To meet our safety standards, autonomous vehicles must have a multi-sensor approach before we allow them on the Lyft platform. To be clear, this policy applies to fully driverless vehicles operating on the Lyft platform, not to driver-assistance features used by human drivers. If you drive on Lyft and use a driver-assist technology in your own vehicle, this policy does not affect you. Today, we’re updating our AV Partner Safety Evaluation Framework to reflect this, requiring any autonomous vehicle operating on the Lyft platform to implement a multi-modal redundant perception system with sensor diversity. Some AV systems use multiple, overlapping sensor types — including cameras, radar and LiDAR — so that if one fails or is temporarily impaired, the vehicle can continue to operate safely. Other sensor architectures rely on a single sensor type, and each has limitations specific to that technology. For instance, cameras can be blinded by glare, fog, and