waymo robo taxis crash 68 less than humans just not in texasWaymo's robotaxis posted a substantially lower crash rate than human drivers across a new four-city study, though Austin stood out as the exception in the data. The Insurance Institute for Highway Safety analyzed 50 million miles of Waymo driving from 2021 through 2024 in San Francisco, Los Angeles, Phoenix, and Austin, then compared that record with 222 billion miles driven by humans in the same cities over the same period.Across the full sample, Waymo vehicles had a 68 percent lower crash rate than human drivers. The IIHS data also showed the autonomous vehicles were involved in 85 percent fewer single-vehicle crashes and 81 percent fewer injury crashes than human-operated vehicles. The study focused on Waymo's Level 4 vehicles in driverless operation and compared them with crash patterns from broadly similar deployment areas.waymo robo taxis crash 68 less than humans just not in texasThe city-level results were less uniform. In Los Angeles, Waymo vehicles recorded a 71 percent lower crash rate than human drivers. In Phoenix, the figure was 76 percent lower. San Francisco was closer, at 35 percent lower. Austin moved in the other direction, with Waymo vehicles showing a crash rate 4 percent higher than human drivers, which is partly attributed to the small sample size.AdvertisementAdvertisementThe study counted 736 reported crashes involving Waymo autonomous vehicles during the period analyzed. The IIHS determined that only 22 percent of those were likely to be "police-reportable," and just 64 occurred while the vehicle was operating in autonomous mode. Of those autonomous-mode crashes, 31 happened with no occupant in the Waymo vehicle, while 25 involved property damage only.Between Life And Death-Waymo Robotaxis Are Blocking Emergency VehiclesBetween Life And Death-Waymo Robotaxis Are Blocking Emergency VehiclesFor a technology that's supposed to remove friction and revolutionize urban transportation, Waymo's robotaxis are creating a different kind of problem. In a closed-door meeting with the National Highway Traffic Safety Administration (NHTSA) last month, first responders from San Francisco and Austin had nothing nice to say about the robotaxi service. According to an audio recording of the session obtained by WIRED, police, firefighters, and emergency officials say autonomous vehicles-particularly Waymo's-are increasingly getting in the way when seconds matter."I believe the technology was deployed too quickly in too vast amounts, with hundreds of vehicles, when it wasn't really ready," said Austin Police Lieutenant William White, who heads the department's Highway Enforcement Command.Michael AccardiMichael AccardiThe IIHS also cautioned that comparing robotaxi crash records with human-driver data remains difficult. Human crash totals are incomplete because many incidents never make it into police databases. Roughly half of all crashes and about one-third of all injuries are never reported to police, which limits the precision of any direct comparison between autonomous and human-driven fleets.AdvertisementAdvertisementEven with that limitation, the IIHS said the findings supported a lower crash-involvement rate for Waymo's driverless vehicles in the areas studied. The institute concluded that the study "provides further evidence that Waymo's L4 vehicles in driverless operation have lower crash involvement rates than human drivers in reasonably similar deployment areas and they are involved in less egregious types of crashes."Lead author Eric Teoh also warned that the available reporting framework may struggle as autonomous fleets scale. "The present data collection system isn't good enough to allow continuous monitoring of a large-scale expansion," Teoh said.The study gives Waymo one of the more concrete public comparisons yet between its robotaxis and conventional traffic, with the major caveat that human crash reporting remains incomplete. The 50-million-mile Waymo sample showed strong aggregate results, especially in Phoenix and Los Angeles, while Austin demonstrated that the numbers can shift sharply when the local dataset is smaller.