By: Robert Morgan, Engineering Director and Mason Lee, Technical Program Manager
To build an autonomous vehicle (AV), we need to be able to safely and efficiently modify and test its software and hardware stacks. While on-road testing may seem like an effective way to do this, it’s simply unrealistic. It’s estimated that it would take more than 10 billion miles to collect enough data to fully validate a self-driving vehicle — that’s 400,000 trips around the Earth.
Let’s put this into perspective by considering the level of engineering required for a self-driving vehicle to complete a frequent and intrinsic action…
By: Luca Bergamini, Software Engineer; Vladimir Iglovikov, Software Engineer; Filip Hlasek, Engineering Manager; and Peter Ondruska, Head of Level 5 Research
Predicting the behavior of traffic agents around an autonomous vehicle (AV) is one of the key unsolved challenges in reaching full self-driving autonomy. With our Prediction Dataset and L5Kit, you can start building motion prediction models in a free afternoon or weekend — even if you have no prior AV experience. …
By Peter Ondruska, Head of AV Research and Sammy Omari, Head of Motion Planning, Prediction, and Software Controls
Over the last few years, machine learning has become an integral part of self-driving development. It has unlocked significant progress in perception, but it does remain limited in its use across the rest of the autonomy stack, particularly in planning. Unlocking significant progress in behavior planning will trigger the next major wave of success for autonomy and we see that the wise use of machine learning for behavior planning is the key to achieving it. …
By: Robert Morgan, Director of Engineering and Sameer Qureshi, Director of Product Management
We’re excited to announce that our autonomous vehicles (AVs) are back on the road — and that during the shelter in place we continued to make progress by doubling down on simulation. Simulation is an important part of our testing program, enabling us to test beyond road miles.
Testing AVs in the real world is necessary, but can also be limiting. Training inputs like weather and pedestrian behavior are limited to what’s happening in the world at each moment, and it can be unpredictable when you encounter…
By: Sacha Arnoud, Senior Director of Engineering and Peter Ondruska, Head of AV Research
Given how important and complex self-driving is, we at Lyft deeply care about creating an environment where teams can join forces. To accelerate development and hear from diverse perspectives, we’re collaborating with many stakeholders across the self-driving industry. Today, we are reaching out to the research community. We saw firsthand the community’s engagement with our 2019 perception dataset and competition and are following up with a new challenge.
Today, we’re thrilled to share our self-driving prediction dataset — the largest released to date — and announce…
By Luca Del Pero, Engineering Manager; Hugo Grimmett, Level 5 Product Manager; and Peter Ondruska, Head of AV Research
Dodging a pothole. Slamming on the brakes for an unexpected pedestrian. Deciding when it’s safe to enter a busy intersection. Drivers all over the world face unplanned scenarios daily and often must make split-second decisions to ensure safety.
But as autonomous vehicles (AVs) become a mainstream transportation option, the need to make such real-time assessments is no longer isolated to human drivers. …
By John Maddox, Head of Autonomous Vehicle Safety
Lyft’s mission is to improve people’s lives with the world’s best transportation. To us, that means putting people first and focusing on safety. Today’s transportation system has significant room for improvement. In 2018, 36,560 people died on our nation’s roads, according to the National Highway Traffic Safety Administration, including disturbing increases for vulnerable road users (VRUs). Despite the tragedy individual families face, society accepts these numbers as “safe”. We think there is an opportunity for a better way.
This company was founded as an innovator in providing access to transportation, and we…
By: Emil Praun, Principal Engineer and Michael Benisch, Engineering Director
A common challenge when deciding which autonomous problems to solve first is how to balance breadth versus depth. Going broad means expanding the Operational Design Domain (ODD) of situations that an autonomous vehicle (AV) can handle. Example situations include operating on highways and city streets, at night, in the rain, in congested areas, around pedestrians, and around cyclists. Going deep means mastering a specific, narrow ODD to the point of being able to operate reliably and repeatedly without human intervention or supervision.
Both breadth and depth must be solved for…
by Luc Vincent, EVP of Autonomous Technology
Since day one, Lyft’s mission has been to improve people’s lives with the world’s best transportation. We started by innovating ways for people to get around — first enabling neighbors to share car rides, then offering shared bikes and scooters. Over the last two and a half years, we’ve made significant progress toward building self-driving cars. They have the potential to save lives and make transportation more accessible for people, contributing to our mission in a big way.
By: Peter Ondruska, Head of AV Research and Vladimir Iglovikov, Senior Computer Vision Engineer
Neural Information Processing Systems (NeurIPS) is one of the largest machine learning conferences in the world. There’s such high interest in attending that tickets are distributed through a lottery system!
If you weren’t able to make it, you’re in luck. We’ve collected the most relevant self-driving-related papers from the Workshop on Machine Learning for Autonomous Driving and the conference itself.