When researchers began to develop autonomous vehicles, or self-driving cars, the thinking was that these vehicles could eliminate the element of human error that contributes to so many car accidents in Southern California. Yet in the time that autonomous vehicles have been tested, they have not actually proven to be all that safe. Indeed, in some cases, self-driving cars have resulted in serious and fatal injuries, especially to pedestrians. According to a recent article in California StreetsBlog, a new study conducted by researchers at the Insurance Institute for Highway Safety (IIHS) suggests that autonomous vehicles will likely prevent only about one-third of all motor vehicle collisions in California and across the country.
Why are autonomous vehicles so ineffective? Is there a possibility that the technology will improve in the coming years such that self-driving cars will do more to reduce the rates of traffic collisions in California?
IIHS Says Identifying Hazards is Not Enough to Prevent Collisions
The recent IIHS study explains that self-driving cars have the capacity to identify hazards that could cause a car accident, and in the future, these autonomous vehicles could get really good at this type of action. Yet, according to IIHS vice president Jessica Cicchino, “this alone would not prevent the bulk of crashes.” What the IIHS study determined is that “fully self-driving cars will eventually identify hazards better than people,” and being able to identify a hazard prior to a collision is certainly important in preventing injuries and deaths.
However, it is not the only way to avoid injuries and deaths from motor vehicle collisions. In fact, there are many different types of human error that contribute to motor vehicle collisions, and autonomous vehicles cannot prevent all of them.
Different Types of Human Error Cause Car Crashes
The IIHS researchers examined approximately 5,000 car accident reports from the National Motor Vehicle Causation Survey to classify the types of driver error that most frequently contribute to and cause collisions. They put those types of human error into these categories:
- “Sensing and perceiving” errors, which can include, for example, “things like driver distraction, impeded visibility, and failing to recognize hazards before it [is] too late”;
- “Predicting” errors, which might include, for example, situations where “drivers misjudged a gap in traffic, incorrectly estimated how fast another vehicle was going, or made an incorrect assumption about what another road user was going to do”;
- “Planning and deciding errors,” which could include “driving too quickly or too slowly for the road conditions, driving aggressively, or leaving too little following distance from the vehicle ahead”;
- “Execution and performance” errors that may include “inadequate or incorrect evasive maneuvers, overcompensation, and other mistakes in controlling the vehicle”; and
- “Incapacitation,” which involves errors made as a result of intoxication, fatigued driving, or medical conditions.
In general, autonomous vehicles are likely only able to prevent crashes involving incapacitation errors, which account for about 10% of all accidents, and some of the other errors listed above that involve “detecting hazards.” Otherwise, human errors will likely continue to play a role in car crashes, according to the study.
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