Study finds potential risk with self-driving cars: detecting dark-skinned pedestrians
A person with dark skin is more likely to get hit by a self-driving car.
(Web Desk) – A new study from the Georgia Institute of Technology suggests autonomous driving systems may experience more difficulty while detecting pedestrians with dark skin than those with light skin.
This means that if you are a person with dark skin, you may be more likely than your white friends to get hit by a self-driving car.
The researchers responsible for the study had eight image-detection systems for analysing images of pedestrians. The people in the photos were separated into two groups based on how their skin tones aligned with the Fitzpatrick skin type scale, which divides skin tones into six categories.
One group consisted of pedestrians who fell into one of the three lightest categories on the Fitzpatrick scale, while the other group consisted of pedestrians who fell into one of the three darkest categories on the Fitzpatrick scale.
The image-detection systems then attempted to identify all of the pedestrians in the images, and the researchers compared the systems’ abilities to detect light-skinned pedestrians versus dark-skinned pedestrians.
On average, the image-detection systems were 5 percent less accurate at detecting dark-skinned pedestrians, even when the researchers controlled for variables that may have been able to explain the disparity, like pedestrians who were partially blocked from view or the time of day the photo was taken.
The researchers suggested that the differences in pedestrian-detection accuracy could result from not having enough dark-skinned pedestrians in the images used to train the systems, as well as the systems’ insufficient emphasis on learning from the smaller population of dark-skinned pedestrians.
It has been noted that the study has not been peer-reviewed and did not use the same image-detection systems or image sets featured in current self-driving vehicles.
The study recommended companies developing autonomous-driving technology to pay a particular attention to the methods being used for training self-driving vehicles for identifying pedestrians accurately.
