Nuro Driver™ September Release Notes

The Nuro Driver™ is always improving in keeping with our core company value to be 1% better every day. Our September release shows us raising the bar by prioritizing machine learning, upgrading our on-board compute, and upping our testing efficiency. Some resulting improvements include better lane changes, smarter driving and sharper detection.

Read on for all of the details.

New Features & Model Improvements

• Prioritizing Machine Learning: We’ve shifted to using our machine learning system as the primary decision-maker for driving, while our expert rules-based systems serve as safety backups during uncertain situations, underlining our commitment to safety.
• Upgraded Onboard Compute: We’ve begun transitioning our vehicles to our next-gen computing system, offering a significant uplift in compute capacity, with improved manufacturability for scaled production. This supports complex perception and behavior tasks in our autonomous system while preserving optimal performance.
• Lane Changes on Higher Speed Roads: Following a general increase of driving speed to 35 mph in August across our test fleet, this month we enabled lane changes on 35 mph roads. This enhancement broadens our route possibilities, making journeys to a chosen destination more versatile.

More efficient off-board testing

• Night Driving: We significantly increased the coverage of our offline night-time testing, particularly around scenarios involving pedestrians, cyclists, and cross-traffic.
• Expanded Test Coverage: We expanded testing for difficult driving scenarios, upscaling tests involving unprotected maneuvers 20x and narrow roads with oncoming traffic 100x. A 10x speed increase in dashboard loading paves the way for quicker reviews by the engineering team.

1% Better

• Better Driving: Thanks to smarter data collection & handling, we’ve upped our human-like driving performance by almost 16% and decreased nudge events by 8x, enabling even smoother rides.
• Managing Latency: For more efficient feature exchange, we’ve combined parts of our localization and perception models, resulting in a 72% drop in latency and significantly lower memory usage, with no performance compromise.
• Better Detection & Response to Unusual Situations: We’ve focused on sharpening detection of active emergency vehicles and school buses.

Statistical information regarding percentage improvements is based on internal data.