Tesla Autopilot Safety Questioned for 3 Fatal Motorcycle Crashes in 2 Months
Tesla’s Autopilot was involved in a third fatal motorcycle crash this summer, raising questions about the driver-assist system’s ability to operate safely.
The National Highway Traffic Safety Administration already launched investigations into the first two crashes and gathered information on the third crash. More details of the latest crash recently surfaced.
The three fatal crashes occurred in a 51-day span this summer and follow a similar line of events: A person driving a Tesla on Autopilot in the early morning strikes a motorcycle.
The crashes renew questions about whether users of the systems are kept sufficiently engaged and prepared to fully control the vehicle when needed. Research shows that Autopilot users glance away from the road more often, and many believe that their cars drive themselves.
Tesla’s Autopilot system keeps the vehicle in its lane while traveling at a set speed, and drivers are instructed to keep their hands on the steering wheel at all times. Tesla says it detects torque on the wheel and uses a camera near the rear-view mirror to determine driver inattentiveness, and uses alerts to remind drivers to keep their eyes on the road.
On August 26, Ingrid Eva Noon was riding her motorcycle in Palm Beach County, Florida at 2:11 a.m. when an impaired driver using Tesla’s Autopilot impacted the rear of the motorcycle, throwing her onto the windshield and killing her; Driver-assist crash data (that automakers like Tesla must report to NHTSA) was recently revealed that Autopilot was engaged.
On July 24, Landon Embry was killed while riding his Harley-Davidson in Utah around 1:09 am when a Tesla driver using Autopilot collided with the back of his motorcycle.
In Riverside, California, a Tesla driver using Autopilot struck a motorcycle lying on a road on July 7 at 4:47 a.m. The motorcyclist, who had already fallen off the bike after hitting a dividing wall, was killed; The Tesla did not strike the rider, who had already been ejected.
The recent crashes suggest the Tesla system is insufficient, according to motorcycle advocates.
Motorcycle safety advocates say they’re concerned that the software fails to see motorcycles and lulls Tesla drivers into a sense of complacency and inattentiveness. Advocates also say that the government’s vehicle safety regulations do not adequately protect motorcycle riders and that steps should be taken to better protect them, including testing driver-assist systems like Autopilot for motorcycle detection.
The Insurance Institute for Highway Safety found that 19 of 23 vehicles tested for pedestrian detection earned a “superior” or “advanced” rating during the daytime, but only four received a “superior” rating at night. More than half earned a basic score or no credit.
Visibility is a challenge for humans and machines at night as there’s less light reflecting off things on the road. Tesla cautions in its vehicle owners’ manuals that many factors can impact Autopilot’s performance, including poor visibility.
“Never depend on these [Autopilot] components to keep you safe,” Tesla says. “It is the driver’s responsibility to stay alert, drive safely, and be in control of the vehicle at all times.”
Tesla has said it relies exclusively on cameras to detect objects on the road and to determine if a driver has their eyes on the road.
Tesla competitors General Motors and Ford use infrared sensors in their vehicles, which can see some objects better when there’s less visible light, in order to better see a driver’s face and detect distracted driving in low light conditions.
The American Motorcyclist Association has cautioned for years about the risks of emerging driving technologies not adequately detecting motorcyclists.
For years it has urged the National Highway Traffic Safety Administration to test for motorcycle detection as it assesses the safety of new vehicles, including their driver-assist technologies. (NHTSA declined to comment on why it does not do so—Europe’s vehicle safety programs test if driver-assist systems identify motorcycles.)
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