The difference in average precision in bounding boxes between the LS (light skin) and DS (dark skin) observed by the researchers was around 1-10%. Not "can't recognize". Notably, people with dark skin were underrepresented in the dataset that the researchers used.
When they compensated for the dataset imbalance by reweighting examples, the issue was resolved.
Notably, the research itself is on publicly-available object detection systems, not any actual autonomous vehicles. Any company seriously building an AV 1) has their own diverse datasets collected from real-world driving and 2) has almost certainly already handled this issue (skin color is a blessedly minor source of variation in pedestrian detection, compared to the tremendous headaches caused by clothing, costumes, carrying other objects, all of which need to be accounted for...).
The paper is good a reminder of the importance of fine-tuning any pedestrian detection system to fix imbalances in your dataset, and avoid false negatives. But it's not what the title claims.