Waymo study analyzes collisions with vulnerable road users

Google’s driverless ride-hailing company Waymo has revealed the latest in a string of AI research-related announcements, this time sharing results from a first-of-its-kind study on collisions with vulnerable road users.

Waymo last week announced EMMA, its end-to-end, multimodal research model for autonomous driving, though it isn’t being used commercially at this point. The company also went on to detail its current approach to AI in a separate press release, noting its continued research into real-world models and AI training, a concept that may sound familiar to those who have followed Tesla’s Full Self-Driving (FSD) development.

On Monday, however, Waymo shared results from a study on Vulnerable Road Users (VRUs), in which the company reconstructed hundreds of collisions involving VRUs like pedestrians, cyclists, and motorcyclists. Waymo says that the research, as conducted in a partnership with Nexar, is the largest dataset of its kind in the U.S., providing key insights into real-world crash scenarios.

The companies analyzed dash camera footage of 335 collisions involving VRUs across six U.S. cities, leveraging over 500 million miles of driving data from Nexar for the research. The partnership also worked with Waymo research partner VUFO, which contributed to the below models on collision injury risk.

Credit: Waymo

Credit: Waymo

According to the National Highway Traffic Safety Administration (NHTSA), there were an estimated 7,522 pedestrians and 1,105 cyclists who lost their lives in traffic crashes in 2022, though Waymo says VRU collision data remains scarce. The company explains this by noting that several VRU-related incidents don’t get reported to authorities or insurance firms, while the U.S. and other major accident databases lack data on these particular incidents.

“We’re excited to partner with Waymo on this cutting-edge research,” said Henrik Liers, Managing Director of Waymo research partner VUFO. “Our common work addresses critical limitations in existing models and provides a more precise, interdisciplinary approach to assessing injury risk for vulnerable road users. This is a significant contribution towards improving road safety.”

Tesla’s Full Self-Driving and end-to-end learning models

The recent developments from Waymo come as General Motors’s (GM’s) Cruise, Amazon’s Zoox, and Tesla’s Supervised Full Self-Driving (FSD) aim to offer similar robotaxi services. While Tesla doesn’t currently operate a paid ride-hailing service as Waymo does, it unveiled the two-seat, steering wheel-free Cybercab robotaxi last month.

Interestingly, Tesla’s FSD system has been touted by some as a more scalable solution, in no small part due to its end-to-end system that trains on millions of clips of real-time driving footage. Another factor is that FSD is available to any Tesla owner who purchases the software, meaning that its neural network stands to have a much wider potential research base than systems like Waymo—at least until services scale up substantially.

Tesla’s Cybercab isn’t expected to go into production until 2026, though FSD Supervised will certainly collect a substantial amount of data in the meantime. As of Tesla’s Q3 earnings call, the company has over 2 billion cumulative miles of data from FSD Supervised users, after it surpassed a milestone of 1.3 billion miles in April.

Tesla FSD V13 to implement features required for unsupervised driving: exec

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